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Meta-Analysis of Transfer Learning for Segmentation of Brain Lesions (2306.11714v1)

Published 20 Jun 2023 in eess.IV, cs.CV, and cs.LG

Abstract: A major challenge in stroke research and stroke recovery predictions is the determination of a stroke lesion's extent and its impact on relevant brain systems. Manual segmentation of stroke lesions from 3D magnetic resonance (MR) imaging volumes, the current gold standard, is not only very time-consuming, but its accuracy highly depends on the operator's experience. As a result, there is a need for a fully automated segmentation method that can efficiently and objectively measure lesion extent and the impact of each lesion to predict impairment and recovery potential which might be beneficial for clinical, translational, and research settings. We have implemented and tested a fully automatic method for stroke lesion segmentation which was developed using eight different 2D-model architectures trained via transfer learning (TL) and mixed data approaches. Additionally, the final prediction was made using a novel ensemble method involving stacking and agreement window. Our novel method was evaluated in a novel in-house dataset containing 22 T1w brain MR images, which were challenging in various perspectives, but mostly because they included T1w MR images from the subacute (which typically less well defined T1 lesions) and chronic stroke phase (which typically means well defined T1-lesions). Cross-validation results indicate that our new method can efficiently and automatically segment lesions fast and with high accuracy compared to ground truth. In addition to segmentation, we provide lesion volume and weighted lesion load of relevant brain systems based on the lesions' overlap with a canonical structural motor system that stretches from the cortical motor region to the lowest end of the brain stem.

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References (28)
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IEEE transactions on medical imaging 37, 2149–2160 (2018). [7] Hu, X. et al. Brain segnet: 3d local refinement network for brain lesion segmentation. BMC medical imaging 20, 1–10 (2020). [8] Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Tsao, C. W. et al. Heart disease and stroke statistics—2022 update: a report from the american heart association. Circulation 145, e153–e639 (2022). [3] Tsao, C. W. et al. Heart disease and stroke statistics—2023 update: a report from the american heart association. Circulation 147, e93–e621 (2023). [4] Atkins, M. S. & Mackiewich, B. T. Fully automatic segmentation of the brain in mri. IEEE transactions on medical imaging 17, 98–107 (1998). [5] Wilke, M., de Haan, B., Juenger, H. & Karnath, H.-O. Manual, semi-automated, and automated delineation of chronic brain lesions: a comparison of methods. NeuroImage 56, 2038–2046 (2011). [6] Zhang, R. et al. Automatic segmentation of acute ischemic stroke from dwi using 3-d fully convolutional densenets. IEEE transactions on medical imaging 37, 2149–2160 (2018). [7] Hu, X. et al. Brain segnet: 3d local refinement network for brain lesion segmentation. BMC medical imaging 20, 1–10 (2020). [8] Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Tsao, C. W. et al. Heart disease and stroke statistics—2023 update: a report from the american heart association. Circulation 147, e93–e621 (2023). [4] Atkins, M. S. & Mackiewich, B. T. Fully automatic segmentation of the brain in mri. IEEE transactions on medical imaging 17, 98–107 (1998). [5] Wilke, M., de Haan, B., Juenger, H. & Karnath, H.-O. Manual, semi-automated, and automated delineation of chronic brain lesions: a comparison of methods. NeuroImage 56, 2038–2046 (2011). [6] Zhang, R. et al. Automatic segmentation of acute ischemic stroke from dwi using 3-d fully convolutional densenets. IEEE transactions on medical imaging 37, 2149–2160 (2018). [7] Hu, X. et al. Brain segnet: 3d local refinement network for brain lesion segmentation. BMC medical imaging 20, 1–10 (2020). [8] Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Atkins, M. S. & Mackiewich, B. T. Fully automatic segmentation of the brain in mri. IEEE transactions on medical imaging 17, 98–107 (1998). [5] Wilke, M., de Haan, B., Juenger, H. & Karnath, H.-O. Manual, semi-automated, and automated delineation of chronic brain lesions: a comparison of methods. NeuroImage 56, 2038–2046 (2011). [6] Zhang, R. et al. Automatic segmentation of acute ischemic stroke from dwi using 3-d fully convolutional densenets. IEEE transactions on medical imaging 37, 2149–2160 (2018). [7] Hu, X. et al. Brain segnet: 3d local refinement network for brain lesion segmentation. BMC medical imaging 20, 1–10 (2020). [8] Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Wilke, M., de Haan, B., Juenger, H. & Karnath, H.-O. Manual, semi-automated, and automated delineation of chronic brain lesions: a comparison of methods. NeuroImage 56, 2038–2046 (2011). [6] Zhang, R. et al. Automatic segmentation of acute ischemic stroke from dwi using 3-d fully convolutional densenets. IEEE transactions on medical imaging 37, 2149–2160 (2018). [7] Hu, X. et al. Brain segnet: 3d local refinement network for brain lesion segmentation. BMC medical imaging 20, 1–10 (2020). [8] Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhang, R. et al. Automatic segmentation of acute ischemic stroke from dwi using 3-d fully convolutional densenets. IEEE transactions on medical imaging 37, 2149–2160 (2018). [7] Hu, X. et al. Brain segnet: 3d local refinement network for brain lesion segmentation. BMC medical imaging 20, 1–10 (2020). [8] Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Hu, X. et al. Brain segnet: 3d local refinement network for brain lesion segmentation. BMC medical imaging 20, 1–10 (2020). [8] Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. 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[24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009).
  2. Tsao, C. W. et al. Heart disease and stroke statistics—2022 update: a report from the american heart association. Circulation 145, e153–e639 (2022). [3] Tsao, C. W. et al. Heart disease and stroke statistics—2023 update: a report from the american heart association. Circulation 147, e93–e621 (2023). [4] Atkins, M. S. & Mackiewich, B. T. Fully automatic segmentation of the brain in mri. IEEE transactions on medical imaging 17, 98–107 (1998). [5] Wilke, M., de Haan, B., Juenger, H. & Karnath, H.-O. Manual, semi-automated, and automated delineation of chronic brain lesions: a comparison of methods. NeuroImage 56, 2038–2046 (2011). [6] Zhang, R. et al. Automatic segmentation of acute ischemic stroke from dwi using 3-d fully convolutional densenets. IEEE transactions on medical imaging 37, 2149–2160 (2018). [7] Hu, X. et al. Brain segnet: 3d local refinement network for brain lesion segmentation. BMC medical imaging 20, 1–10 (2020). [8] Yu, W., Huang, Z., Zhang, J. & Shan, H. 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[19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Tsao, C. W. et al. Heart disease and stroke statistics—2023 update: a report from the american heart association. Circulation 147, e93–e621 (2023). [4] Atkins, M. S. & Mackiewich, B. T. Fully automatic segmentation of the brain in mri. IEEE transactions on medical imaging 17, 98–107 (1998). [5] Wilke, M., de Haan, B., Juenger, H. & Karnath, H.-O. Manual, semi-automated, and automated delineation of chronic brain lesions: a comparison of methods. NeuroImage 56, 2038–2046 (2011). [6] Zhang, R. et al. Automatic segmentation of acute ischemic stroke from dwi using 3-d fully convolutional densenets. IEEE transactions on medical imaging 37, 2149–2160 (2018). [7] Hu, X. et al. Brain segnet: 3d local refinement network for brain lesion segmentation. BMC medical imaging 20, 1–10 (2020). [8] Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Atkins, M. S. & Mackiewich, B. T. Fully automatic segmentation of the brain in mri. IEEE transactions on medical imaging 17, 98–107 (1998). [5] Wilke, M., de Haan, B., Juenger, H. & Karnath, H.-O. Manual, semi-automated, and automated delineation of chronic brain lesions: a comparison of methods. NeuroImage 56, 2038–2046 (2011). [6] Zhang, R. et al. Automatic segmentation of acute ischemic stroke from dwi using 3-d fully convolutional densenets. IEEE transactions on medical imaging 37, 2149–2160 (2018). [7] Hu, X. et al. Brain segnet: 3d local refinement network for brain lesion segmentation. BMC medical imaging 20, 1–10 (2020). [8] Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Wilke, M., de Haan, B., Juenger, H. & Karnath, H.-O. Manual, semi-automated, and automated delineation of chronic brain lesions: a comparison of methods. NeuroImage 56, 2038–2046 (2011). [6] Zhang, R. et al. Automatic segmentation of acute ischemic stroke from dwi using 3-d fully convolutional densenets. IEEE transactions on medical imaging 37, 2149–2160 (2018). [7] Hu, X. et al. Brain segnet: 3d local refinement network for brain lesion segmentation. BMC medical imaging 20, 1–10 (2020). [8] Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhang, R. et al. Automatic segmentation of acute ischemic stroke from dwi using 3-d fully convolutional densenets. IEEE transactions on medical imaging 37, 2149–2160 (2018). [7] Hu, X. et al. Brain segnet: 3d local refinement network for brain lesion segmentation. BMC medical imaging 20, 1–10 (2020). [8] Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Hu, X. et al. Brain segnet: 3d local refinement network for brain lesion segmentation. BMC medical imaging 20, 1–10 (2020). [8] Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. 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[20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Atkins, M. S. & Mackiewich, B. T. Fully automatic segmentation of the brain in mri. IEEE transactions on medical imaging 17, 98–107 (1998). [5] Wilke, M., de Haan, B., Juenger, H. & Karnath, H.-O. Manual, semi-automated, and automated delineation of chronic brain lesions: a comparison of methods. NeuroImage 56, 2038–2046 (2011). [6] Zhang, R. et al. Automatic segmentation of acute ischemic stroke from dwi using 3-d fully convolutional densenets. IEEE transactions on medical imaging 37, 2149–2160 (2018). [7] Hu, X. et al. Brain segnet: 3d local refinement network for brain lesion segmentation. BMC medical imaging 20, 1–10 (2020). [8] Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Wilke, M., de Haan, B., Juenger, H. & Karnath, H.-O. Manual, semi-automated, and automated delineation of chronic brain lesions: a comparison of methods. NeuroImage 56, 2038–2046 (2011). [6] Zhang, R. et al. Automatic segmentation of acute ischemic stroke from dwi using 3-d fully convolutional densenets. IEEE transactions on medical imaging 37, 2149–2160 (2018). [7] Hu, X. et al. Brain segnet: 3d local refinement network for brain lesion segmentation. BMC medical imaging 20, 1–10 (2020). [8] Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhang, R. et al. Automatic segmentation of acute ischemic stroke from dwi using 3-d fully convolutional densenets. IEEE transactions on medical imaging 37, 2149–2160 (2018). [7] Hu, X. et al. Brain segnet: 3d local refinement network for brain lesion segmentation. BMC medical imaging 20, 1–10 (2020). [8] Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Hu, X. et al. Brain segnet: 3d local refinement network for brain lesion segmentation. BMC medical imaging 20, 1–10 (2020). [8] Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. 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[19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. 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IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. 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Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. 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Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009).
  4. Fully automatic segmentation of the brain in mri. IEEE transactions on medical imaging 17, 98–107 (1998). [5] Wilke, M., de Haan, B., Juenger, H. & Karnath, H.-O. Manual, semi-automated, and automated delineation of chronic brain lesions: a comparison of methods. NeuroImage 56, 2038–2046 (2011). [6] Zhang, R. et al. Automatic segmentation of acute ischemic stroke from dwi using 3-d fully convolutional densenets. IEEE transactions on medical imaging 37, 2149–2160 (2018). [7] Hu, X. et al. Brain segnet: 3d local refinement network for brain lesion segmentation. BMC medical imaging 20, 1–10 (2020). [8] Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Wilke, M., de Haan, B., Juenger, H. & Karnath, H.-O. Manual, semi-automated, and automated delineation of chronic brain lesions: a comparison of methods. NeuroImage 56, 2038–2046 (2011). [6] Zhang, R. et al. Automatic segmentation of acute ischemic stroke from dwi using 3-d fully convolutional densenets. IEEE transactions on medical imaging 37, 2149–2160 (2018). [7] Hu, X. et al. Brain segnet: 3d local refinement network for brain lesion segmentation. BMC medical imaging 20, 1–10 (2020). [8] Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhang, R. et al. Automatic segmentation of acute ischemic stroke from dwi using 3-d fully convolutional densenets. IEEE transactions on medical imaging 37, 2149–2160 (2018). [7] Hu, X. et al. Brain segnet: 3d local refinement network for brain lesion segmentation. BMC medical imaging 20, 1–10 (2020). [8] Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Hu, X. et al. Brain segnet: 3d local refinement network for brain lesion segmentation. BMC medical imaging 20, 1–10 (2020). [8] Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. 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Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. 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Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. 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D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009).
  5. Manual, semi-automated, and automated delineation of chronic brain lesions: a comparison of methods. NeuroImage 56, 2038–2046 (2011). [6] Zhang, R. et al. Automatic segmentation of acute ischemic stroke from dwi using 3-d fully convolutional densenets. IEEE transactions on medical imaging 37, 2149–2160 (2018). [7] Hu, X. et al. Brain segnet: 3d local refinement network for brain lesion segmentation. BMC medical imaging 20, 1–10 (2020). [8] Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhang, R. et al. Automatic segmentation of acute ischemic stroke from dwi using 3-d fully convolutional densenets. IEEE transactions on medical imaging 37, 2149–2160 (2018). [7] Hu, X. et al. Brain segnet: 3d local refinement network for brain lesion segmentation. BMC medical imaging 20, 1–10 (2020). [8] Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Hu, X. et al. Brain segnet: 3d local refinement network for brain lesion segmentation. BMC medical imaging 20, 1–10 (2020). [8] Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. 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Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. 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Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. 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Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. 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[21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009).
  6. Zhang, R. et al. Automatic segmentation of acute ischemic stroke from dwi using 3-d fully convolutional densenets. IEEE transactions on medical imaging 37, 2149–2160 (2018). [7] Hu, X. et al. Brain segnet: 3d local refinement network for brain lesion segmentation. BMC medical imaging 20, 1–10 (2020). [8] Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Hu, X. et al. Brain segnet: 3d local refinement network for brain lesion segmentation. BMC medical imaging 20, 1–10 (2020). [8] Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009).
  7. Hu, X. et al. Brain segnet: 3d local refinement network for brain lesion segmentation. BMC medical imaging 20, 1–10 (2020). [8] Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, W., Huang, Z., Zhang, J. & Shan, H. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. 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Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009).
  8. San-net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization. Computers in Biology and Medicine 156, 106717 (2023). [9] Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019). [10] Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. 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Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. 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D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009).
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Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Tajbakhsh, N. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35, 1299–1312 (2016). [11] Shin, H.-C. et al. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging 35, 1285–1298 (2016). [12] He, K. et al. Hf-unet: learning hierarchically inter-task relevance in multi-task u-net for accurate prostate segmentation in ct images. IEEE Transactions on Medical Imaging 40, 2118–2128 (2021). [13] Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Shin, H.-C. et al. 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Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. 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Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. 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Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. 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Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. 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Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. 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Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. 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Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Kang, Q. et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical image analysis 79, 102443 (2022). [14] Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Gryska, E., Schneiderman, J., Björkman-Burtscher, I. & Heckemann, R. A. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. 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Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. 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D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. 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Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. 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[27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009).
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[19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. 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Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. 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IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. 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Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. 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IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. 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IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. 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Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. 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A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009).
  14. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ open 11, e042660 (2021). [15] Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009).
  15. Zhu, W. et al. Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46, 576–589 (2019). [16] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [17] Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Chollet, F. Building powerful image classification models using very little data. Keras Blog 5, 90–95 (2016). [18] Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. 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[21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. 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[22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. 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[23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. 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Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. 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Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009).
  18. Lancaster, J. et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human brain mapping 5, 238–242 (1997). [19] Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009).
  19. Lancaster, J. L. et al. Automated talairach atlas labels for functional brain mapping. Human brain mapping 10, 120–131 (2000). [20] Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhu, L. L., Lindenberg, R., Alexander, M. P. & Schlaug, G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009).
  20. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41, 910–915 (2010). [21] Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009).
  21. Feng, W. et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Annals of neurology 78, 860–870 (2015). [22] Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Zhou, Y., Huang, W., Dong, P., Xia, Y. & Wang, S. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009).
  22. D-unet: a dimension-fusion u shape network for chronic stroke lesion segmentation. IEEE/ACM transactions on computational biology and bioinformatics 18, 940–950 (2019). [23] Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Shin, H., Agyeman, R., Rafiq, M., Chang, M. C. & Choi, G. S. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009).
  23. Automated segmentation of chronic stroke lesion using efficient u-net architecture. Biocybernetics and Biomedical Engineering 42, 285–294 (2022). [24] Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Verma, K., Kumar, S. & Paydarfar, D. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009).
  24. Automatic segmentation and quantitative assessment of stroke lesions on mr images. Diagnostics 12, 2055 (2022). [25] Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009).
  25. Bao, Q. et al. Mdan: mirror difference aware network for brain stroke lesion segmentation. IEEE Journal of Biomedical and Health Informatics 26, 1628–1639 (2021). [26] Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009).
  26. Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12, 822–838 (2013). [27] DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009).
  27. DeVetten, G. et al. Acute corticospinal tract wallerian degeneration is associated with stroke outcome. Stroke 41, 751–756 (2010). [28] Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009). Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009).
  28. Yu, C. et al. A longitudinal diffusion tensor imaging study on wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage 47, 451–458 (2009).
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