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Segmentation of tibiofemoral joint tissues from knee MRI using MtRA-Unet and incorporating shape information: Data from the Osteoarthritis Initiative (2401.12932v1)

Published 23 Jan 2024 in eess.IV and cs.CV

Abstract: Knee Osteoarthritis (KOA) is the third most prevalent Musculoskeletal Disorder (MSD) after neck and back pain. To monitor such a severe MSD, a segmentation map of the femur, tibia and tibiofemoral cartilage is usually accessed using the automated segmentation algorithm from the Magnetic Resonance Imaging (MRI) of the knee. But, in recent works, such segmentation is conceivable only from the multistage framework thus creating data handling issues and needing continuous manual inference rendering it unable to make a quick and precise clinical diagnosis. In order to solve these issues, in this paper the Multi-Resolution Attentive-Unet (MtRA-Unet) is proposed to segment the femur, tibia and tibiofemoral cartilage automatically. The proposed work has included a novel Multi-Resolution Feature Fusion (MRFF) and Shape Reconstruction (SR) loss that focuses on multi-contextual information and structural anatomical details of the femur, tibia and tibiofemoral cartilage. Unlike previous approaches, the proposed work is a single-stage and end-to-end framework producing a Dice Similarity Coefficient (DSC) of 98.5% for the femur, 98.4% for the tibia, 89.1% for Femoral Cartilage (FC) and 86.1% for Tibial Cartilage (TC) for critical MRI slices that can be helpful to clinicians for KOA grading. The time to segment MRI volume (160 slices) per subject is 22 sec. which is one of the fastest among state-of-the-art. Moreover, comprehensive experimentation on the segmentation of FC and TC which is of utmost importance for morphology-based studies to check KOA progression reveals that the proposed method has produced an excellent result with binary segmentation

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References (34)
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[2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Deng, Y., You, L., Wang, Y., Zhou, X.: A coarse-to-fine framework for automated knee bone and cartilage segmentation data from the osteoarthritis initiative. Journal of Digital Imaging 34(4), 833–840 (2021) Ahmed and Mstafa [2022] Ahmed, S.M., Mstafa, R.J.: A comprehensive survey on bone segmentation techniques in knee osteoarthritis research: From conventional methods to deep learning. Diagnostics 12(3), 611 (2022) Ambellan et al. [2019] Ambellan, F., Tack, A., Ehlke, M., Zachow, S.: Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the osteoarthritis initiative. Medical image analysis 52, 109–118 (2019) Sorensen [1948] Sorensen, T.A.: A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on danish commons. Biol. Skar. 5, 1–34 (1948) Shannon [1948] Shannon, C.E.: A mathematical theory of communication. The Bell system technical journal 27(3), 379–423 (1948) Al Arif et al. [2017] Al Arif, S., Knapp, K., Slabaugh, G.: Shape-aware deep convolutional neural network for vertebrae segmentation. In: International Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging, pp. 12–24 (2017). Springer Mosinska et al. [2018] Mosinska, A., Marquez-Neila, P., Koziński, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) Kim and Ye [2019] Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Ahmed, S.M., Mstafa, R.J.: A comprehensive survey on bone segmentation techniques in knee osteoarthritis research: From conventional methods to deep learning. Diagnostics 12(3), 611 (2022) Ambellan et al. [2019] Ambellan, F., Tack, A., Ehlke, M., Zachow, S.: Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the osteoarthritis initiative. Medical image analysis 52, 109–118 (2019) Sorensen [1948] Sorensen, T.A.: A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on danish commons. Biol. Skar. 5, 1–34 (1948) Shannon [1948] Shannon, C.E.: A mathematical theory of communication. The Bell system technical journal 27(3), 379–423 (1948) Al Arif et al. [2017] Al Arif, S., Knapp, K., Slabaugh, G.: Shape-aware deep convolutional neural network for vertebrae segmentation. In: International Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging, pp. 12–24 (2017). Springer Mosinska et al. [2018] Mosinska, A., Marquez-Neila, P., Koziński, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) Kim and Ye [2019] Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Ambellan, F., Tack, A., Ehlke, M., Zachow, S.: Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the osteoarthritis initiative. Medical image analysis 52, 109–118 (2019) Sorensen [1948] Sorensen, T.A.: A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on danish commons. Biol. Skar. 5, 1–34 (1948) Shannon [1948] Shannon, C.E.: A mathematical theory of communication. The Bell system technical journal 27(3), 379–423 (1948) Al Arif et al. [2017] Al Arif, S., Knapp, K., Slabaugh, G.: Shape-aware deep convolutional neural network for vertebrae segmentation. In: International Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging, pp. 12–24 (2017). Springer Mosinska et al. [2018] Mosinska, A., Marquez-Neila, P., Koziński, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) Kim and Ye [2019] Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. 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The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. 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In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) Kim and Ye [2019] Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. 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[2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Shannon, C.E.: A mathematical theory of communication. The Bell system technical journal 27(3), 379–423 (1948) Al Arif et al. [2017] Al Arif, S., Knapp, K., Slabaugh, G.: Shape-aware deep convolutional neural network for vertebrae segmentation. In: International Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging, pp. 12–24 (2017). Springer Mosinska et al. [2018] Mosinska, A., Marquez-Neila, P., Koziński, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) Kim and Ye [2019] Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Al Arif, S., Knapp, K., Slabaugh, G.: Shape-aware deep convolutional neural network for vertebrae segmentation. In: International Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging, pp. 12–24 (2017). Springer Mosinska et al. [2018] Mosinska, A., Marquez-Neila, P., Koziński, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) Kim and Ye [2019] Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Mosinska, A., Marquez-Neila, P., Koziński, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) Kim and Ye [2019] Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. 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[2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. 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The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. 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Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. 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The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. 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IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. 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[2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. 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American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. 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  2. Deng, Y., You, L., Wang, Y., Zhou, X.: A coarse-to-fine framework for automated knee bone and cartilage segmentation data from the osteoarthritis initiative. Journal of Digital Imaging 34(4), 833–840 (2021) Ahmed and Mstafa [2022] Ahmed, S.M., Mstafa, R.J.: A comprehensive survey on bone segmentation techniques in knee osteoarthritis research: From conventional methods to deep learning. Diagnostics 12(3), 611 (2022) Ambellan et al. [2019] Ambellan, F., Tack, A., Ehlke, M., Zachow, S.: Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the osteoarthritis initiative. Medical image analysis 52, 109–118 (2019) Sorensen [1948] Sorensen, T.A.: A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on danish commons. Biol. 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In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Ahmed, S.M., Mstafa, R.J.: A comprehensive survey on bone segmentation techniques in knee osteoarthritis research: From conventional methods to deep learning. Diagnostics 12(3), 611 (2022) Ambellan et al. [2019] Ambellan, F., Tack, A., Ehlke, M., Zachow, S.: Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the osteoarthritis initiative. Medical image analysis 52, 109–118 (2019) Sorensen [1948] Sorensen, T.A.: A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on danish commons. Biol. Skar. 5, 1–34 (1948) Shannon [1948] Shannon, C.E.: A mathematical theory of communication. The Bell system technical journal 27(3), 379–423 (1948) Al Arif et al. [2017] Al Arif, S., Knapp, K., Slabaugh, G.: Shape-aware deep convolutional neural network for vertebrae segmentation. In: International Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging, pp. 12–24 (2017). Springer Mosinska et al. [2018] Mosinska, A., Marquez-Neila, P., Koziński, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) Kim and Ye [2019] Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Ambellan, F., Tack, A., Ehlke, M., Zachow, S.: Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the osteoarthritis initiative. Medical image analysis 52, 109–118 (2019) Sorensen [1948] Sorensen, T.A.: A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on danish commons. Biol. Skar. 5, 1–34 (1948) Shannon [1948] Shannon, C.E.: A mathematical theory of communication. The Bell system technical journal 27(3), 379–423 (1948) Al Arif et al. [2017] Al Arif, S., Knapp, K., Slabaugh, G.: Shape-aware deep convolutional neural network for vertebrae segmentation. In: International Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging, pp. 12–24 (2017). Springer Mosinska et al. [2018] Mosinska, A., Marquez-Neila, P., Koziński, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) Kim and Ye [2019] Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. 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IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Sorensen, T.A.: A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on danish commons. Biol. Skar. 5, 1–34 (1948) Shannon [1948] Shannon, C.E.: A mathematical theory of communication. The Bell system technical journal 27(3), 379–423 (1948) Al Arif et al. [2017] Al Arif, S., Knapp, K., Slabaugh, G.: Shape-aware deep convolutional neural network for vertebrae segmentation. In: International Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging, pp. 12–24 (2017). Springer Mosinska et al. [2018] Mosinska, A., Marquez-Neila, P., Koziński, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) Kim and Ye [2019] Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Shannon, C.E.: A mathematical theory of communication. The Bell system technical journal 27(3), 379–423 (1948) Al Arif et al. [2017] Al Arif, S., Knapp, K., Slabaugh, G.: Shape-aware deep convolutional neural network for vertebrae segmentation. In: International Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging, pp. 12–24 (2017). Springer Mosinska et al. [2018] Mosinska, A., Marquez-Neila, P., Koziński, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) Kim and Ye [2019] Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Al Arif, S., Knapp, K., Slabaugh, G.: Shape-aware deep convolutional neural network for vertebrae segmentation. In: International Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging, pp. 12–24 (2017). Springer Mosinska et al. [2018] Mosinska, A., Marquez-Neila, P., Koziński, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) Kim and Ye [2019] Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Mosinska, A., Marquez-Neila, P., Koziński, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) Kim and Ye [2019] Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. 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[2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. 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The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. 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Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. 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The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. 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IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. 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[2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Ambellan, F., Tack, A., Ehlke, M., Zachow, S.: Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the osteoarthritis initiative. Medical image analysis 52, 109–118 (2019) Sorensen [1948] Sorensen, T.A.: A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on danish commons. Biol. Skar. 5, 1–34 (1948) Shannon [1948] Shannon, C.E.: A mathematical theory of communication. The Bell system technical journal 27(3), 379–423 (1948) Al Arif et al. [2017] Al Arif, S., Knapp, K., Slabaugh, G.: Shape-aware deep convolutional neural network for vertebrae segmentation. In: International Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging, pp. 12–24 (2017). Springer Mosinska et al. [2018] Mosinska, A., Marquez-Neila, P., Koziński, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) Kim and Ye [2019] Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Sorensen, T.A.: A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on danish commons. Biol. Skar. 5, 1–34 (1948) Shannon [1948] Shannon, C.E.: A mathematical theory of communication. The Bell system technical journal 27(3), 379–423 (1948) Al Arif et al. [2017] Al Arif, S., Knapp, K., Slabaugh, G.: Shape-aware deep convolutional neural network for vertebrae segmentation. In: International Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging, pp. 12–24 (2017). Springer Mosinska et al. [2018] Mosinska, A., Marquez-Neila, P., Koziński, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) Kim and Ye [2019] Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Shannon, C.E.: A mathematical theory of communication. The Bell system technical journal 27(3), 379–423 (1948) Al Arif et al. [2017] Al Arif, S., Knapp, K., Slabaugh, G.: Shape-aware deep convolutional neural network for vertebrae segmentation. In: International Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging, pp. 12–24 (2017). Springer Mosinska et al. [2018] Mosinska, A., Marquez-Neila, P., Koziński, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) Kim and Ye [2019] Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Al Arif, S., Knapp, K., Slabaugh, G.: Shape-aware deep convolutional neural network for vertebrae segmentation. In: International Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging, pp. 12–24 (2017). Springer Mosinska et al. [2018] Mosinska, A., Marquez-Neila, P., Koziński, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) Kim and Ye [2019] Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Mosinska, A., Marquez-Neila, P., Koziński, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) Kim and Ye [2019] Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. 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IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. 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IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. 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Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. 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[2018] Mosinska, A., Marquez-Neila, P., Koziński, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) Kim and Ye [2019] Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Sorensen, T.A.: A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on danish commons. Biol. Skar. 5, 1–34 (1948) Shannon [1948] Shannon, C.E.: A mathematical theory of communication. The Bell system technical journal 27(3), 379–423 (1948) Al Arif et al. [2017] Al Arif, S., Knapp, K., Slabaugh, G.: Shape-aware deep convolutional neural network for vertebrae segmentation. In: International Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging, pp. 12–24 (2017). Springer Mosinska et al. [2018] Mosinska, A., Marquez-Neila, P., Koziński, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) Kim and Ye [2019] Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Shannon, C.E.: A mathematical theory of communication. The Bell system technical journal 27(3), 379–423 (1948) Al Arif et al. [2017] Al Arif, S., Knapp, K., Slabaugh, G.: Shape-aware deep convolutional neural network for vertebrae segmentation. In: International Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging, pp. 12–24 (2017). Springer Mosinska et al. [2018] Mosinska, A., Marquez-Neila, P., Koziński, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) Kim and Ye [2019] Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Al Arif, S., Knapp, K., Slabaugh, G.: Shape-aware deep convolutional neural network for vertebrae segmentation. In: International Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging, pp. 12–24 (2017). Springer Mosinska et al. [2018] Mosinska, A., Marquez-Neila, P., Koziński, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) Kim and Ye [2019] Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Mosinska, A., Marquez-Neila, P., Koziński, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. 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[2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. 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IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. 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The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. 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IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. 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The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. 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In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Shannon, C.E.: A mathematical theory of communication. The Bell system technical journal 27(3), 379–423 (1948) Al Arif et al. [2017] Al Arif, S., Knapp, K., Slabaugh, G.: Shape-aware deep convolutional neural network for vertebrae segmentation. In: International Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging, pp. 12–24 (2017). Springer Mosinska et al. [2018] Mosinska, A., Marquez-Neila, P., Koziński, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) Kim and Ye [2019] Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Al Arif, S., Knapp, K., Slabaugh, G.: Shape-aware deep convolutional neural network for vertebrae segmentation. In: International Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging, pp. 12–24 (2017). Springer Mosinska et al. [2018] Mosinska, A., Marquez-Neila, P., Koziński, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) Kim and Ye [2019] Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. 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The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Mosinska, A., Marquez-Neila, P., Koziński, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) Kim and Ye [2019] Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. 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Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. 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The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. 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Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. 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International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. 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Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Mosinska, A., Marquez-Neila, P., Koziński, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) Kim and Ye [2019] Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. 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[2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. 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Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. 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[2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. 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In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) Kim and Ye [2019] Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. 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In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. 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The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. 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Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. 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IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. 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[2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Kim, B., Ye, J.C.: Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing 29, 1856–1866 (2019) Lambert et al. [2021] Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. 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Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. 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[2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. 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[2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. 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Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. 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In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Lambert, Z., Le Guyader, C., Petitjean, C.: A geometrically-constrained deep network for ct image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 29–33 (2021). IEEE Sarasaen et al. [2021] Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. 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The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., Speck, O.: Fine-tuning deep learning model parameters for improved super-resolution of dynamic mri with prior-knowledge. Artificial Intelligence in Medicine 121, 102196 (2021) El Jurdi et al. [2021] El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. 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Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. 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[2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. 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Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. 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[2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. 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[2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. 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The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. 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Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. 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The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. 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The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 210, 103248 (2021) Lynch et al. [2001] Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Lynch, J.A., Zaim, S., Zhao, J., Peterfy, C.G., Genant, H.K.: Automating measurement of subtle changes in articular cartilage from mri of the knee by combining 3d image registration and segmentation. In: Medical Imaging 2001: Image Processing, vol. 4322, pp. 431–439 (2001). SPIE Fripp et al. [2009] Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. 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The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE transactions on medical imaging 29(1), 55–64 (2009) Williams et al. [2010] Williams, T.G., Vincent, G., Bowes, M., Cootes, T., Balamoody, S., Hutchinson, C., Waterton, J.C., Taylor, C.J.: Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee mri. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. 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International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. 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IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. 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The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. 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In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 432–435 (2010). IEEE Liu et al. [2018] Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. 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Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine 79(4), 2379–2391 (2018) Burton II et al. [2020] Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. 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Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Burton II, W., Myers, C., Rullkoetter, P.: Semi-supervised learning for automatic segmentation of the knee from mri with convolutional neural networks. Computer Methods and Programs in Biomedicine 189, 105328 (2020) Heimann et al. [2010] Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. 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IEEE transactions on medical imaging 39(6), 1856–1867 (2019) Ebrahimkhani et al. [2022] Ebrahimkhani, S., Dharmaratne, A., Jaward, M.H., Wang, Y., Cicuttini, F.M.: Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 467, 36–55 (2022) Liang et al. [2022] Liang, D., Liu, J., Wang, K., Luo, G., Wang, W., Li, S.: Position-prior clustering-based self-attention module for knee cartilage segmentation. arXiv preprint arXiv:2206.10286 (2022) [26] NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. 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IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.: Segmentation of knee images: a grand challenge. In: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic, vol. 1 (2010). Beijing, China Kessler et al. [2020] Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. [2022] Li, Z., Chen, K., Liu, P., Chen, X., Zheng, G.: Entropy and distance maps-guided segmentation of articular cartilage: data from the osteoarthritis initiative. International Journal of Computer Assisted Radiology and Surgery 17(3), 553–560 (2022) Abd Latif and Faye [2021] Abd Latif, M.H., Faye, I.: Automated tibiofemoral joint segmentation based on deeply supervised 2d-3d ensemble u-net: Data from the osteoarthritis initiative. Artificial intelligence in medicine 122, 102213 (2021) Zhou et al. [2019] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. 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IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Kessler, D.A., MacKay, J.W., Crowe, V.A., Henson, F.M., Graves, M.J., Gilbert, F.J., Kaggie, J.D.: The optimisation of deep neural networks for segmenting multiple knee joint tissues from mris. Computerized Medical Imaging and Graphics 86, 101793 (2020) Li et al. 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  25. NDA — nda.nih.gov. https://nda.nih.gov/oai/. [Accessed 31-12-2023] [27] public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. 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Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. 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  26. public data — Zuse Institute Berlin (ZIB) — pubdata.zib.de. https://pubdata.zib.de/. [Accessed 31-12-2023] Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018)
  27. Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer Woo et al. [2018] Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. 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In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018)
  28. Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018) Zhang et al. [2020] Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018)
  29. Zhang, Y., Li, W., Gong, W., Wang, Z., Sun, J.: An improved boundary-aware perceptual loss for building extraction from vhr images. Remote Sensing 12(7), 1195 (2020) Koo et al. [2005] Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018)
  30. Koo, S., Gold, G., Andriacchi, T.: Considerations in measuring cartilage thickness using mri: factors influencing reproducibility and accuracy. Osteoarthritis and cartilage 13(9), 782–789 (2005) Andriacchi et al. [2009] Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018)
  31. Andriacchi, T.P., Koo, S., Scanlan, S.F.: Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee. The Journal of Bone and Joint Surgery. American volume. 91(Suppl 1), 95 (2009) Badrinarayanan et al. [2017] Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39(12), 2481–2495 (2017) Wang et al. [2020] Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence 43(10), 3349–3364 (2020) Yu et al. [2018] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018) Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325–341 (2018)
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