Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
144 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Ultrasound Image Segmentation of Thyroid Nodule via Latent Semantic Feature Co-Registration (2310.09221v2)

Published 13 Oct 2023 in eess.IV and cs.CV

Abstract: Segmentation of nodules in thyroid ultrasound imaging plays a crucial role in the detection and treatment of thyroid cancer. However, owing to the diversity of scanner vendors and imaging protocols in different hospitals, the automatic segmentation model, which has already demonstrated expert-level accuracy in the field of medical image segmentation, finds its accuracy reduced as the result of its weak generalization performance when being applied in clinically realistic environments. To address this issue, the present paper proposes ASTN, a framework for thyroid nodule segmentation achieved through a new type co-registration network. By extracting latent semantic information from the atlas and target images and utilizing in-depth features to accomplish the co-registration of nodules in thyroid ultrasound images, this framework can ensure the integrity of anatomical structure and reduce the impact on segmentation as the result of overall differences in image caused by different devices. In addition, this paper also provides an atlas selection algorithm to mitigate the difficulty of co-registration. As shown by the evaluation results collected from the datasets of different devices, thanks to the method we proposed, the model generalization has been greatly improved while maintaining a high level of segmentation accuracy.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (35)
  1. G. Li, R. Chen, J. Zhang, K. Liu, C. Geng, and L. Lyu, “Fusing enhanced transformer and large kernel cnn for malignant thyroid nodule segmentation,” Biomedical Signal Processing and Control, vol. 83, p. 104636, 2023.
  2. Y. Yuan, C. Li, L. Xu, S. Zhu, Y. Hua, and J. Zhang, “Csm-net: Automatic joint segmentation of intima-media complex and lumen in carotid artery ultrasound images,” Computers in Biology and Medicine, vol. 150, p. 106119, 2022.
  3. Z. Su, K. Yao, X. Yang, K. Huang, Q. Wang, and J. Sun, “Rethinking data augmentation for single-source domain generalization in medical image segmentation,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 2366–2374, 2023.
  4. J. De Fauw, J. R. Ledsam, B. Romera-Paredes, S. Nikolov, N. Tomasev, S. Blackwell, H. Askham, X. Glorot, B. O’Donoghue, D. Visentin, et al., “Clinically applicable deep learning for diagnosis and referral in retinal disease,” Nature medicine, vol. 24, no. 9, pp. 1342–1350, 2018.
  5. L. Zhang, X. Wang, D. Yang, T. Sanford, S. Harmon, B. Turkbey, B. J. Wood, H. Roth, A. Myronenko, D. Xu, et al., “Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation,” IEEE transactions on medical imaging, vol. 39, no. 7, pp. 2531–2540, 2020.
  6. J. Wang, C. Lan, C. Liu, Y. Ouyang, T. Qin, W. Lu, Y. Chen, W. Zeng, and P. Yu, “Generalizing to unseen domains: A survey on domain generalization,” IEEE Transactions on Knowledge and Data Engineering, 2022.
  7. C. Chen, Q. Dou, H. Chen, J. Qin, and P. A. Heng, “Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation,” IEEE transactions on medical imaging, vol. 39, no. 7, pp. 2494–2505, 2020.
  8. C. Chen, Z. Chen, B. Jiang, and X. Jin, “Joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation,” in Proceedings of the AAAI conference on artificial intelligence, vol. 33, pp. 3296–3303, 2019.
  9. M. Sinclair, A. Schuh, K. Hahn, K. Petersen, Y. Bai, J. Batten, M. Schaap, and B. Glocker, “Atlas-istn: joint segmentation, registration and atlas construction with image-and-spatial transformer networks,” Medical Image Analysis, vol. 78, p. 102383, 2022.
  10. I. Isgum, M. Staring, A. Rutten, M. Prokop, M. A. Viergever, and B. Van Ginneken, “Multi-atlas-based segmentation with local decision fusion—application to cardiac and aortic segmentation in ct scans,” IEEE transactions on medical imaging, vol. 28, no. 7, pp. 1000–1010, 2009.
  11. M. Jaderberg, K. Simonyan, A. Zisserman, et al., “Spatial transformer networks,” Advances in neural information processing systems, vol. 28, 2015.
  12. Y. Chen, G. Lin, S. Li, O. Bourahla, Y. Wu, F. Wang, J. Feng, M. Xu, and X. Li, “Banet: Bidirectional aggregation network with occlusion handling for panoptic segmentation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 3793–3802, 2020.
  13. L. Xie, L. E. Wisse, J. Wang, S. Ravikumar, P. Khandelwal, T. Glenn, A. Luther, S. Lim, D. A. Wolk, and P. A. Yushkevich, “Deep label fusion: A generalizable hybrid multi-atlas and deep convolutional neural network for medical image segmentation,” Medical Image Analysis, vol. 83, p. 102683, 2023.
  14. B. Huang, Y. Ye, Z. Xu, Z. Cai, Y. He, Z. Zhong, L. Liu, X. Chen, H. Chen, and B. Huang, “3d lightweight network for simultaneous registration and segmentation of organs-at-risk in ct images of head and neck cancer,” IEEE Transactions on Medical Imaging, vol. 41, no. 4, pp. 951–964, 2021.
  15. J. Zhang, R. Venkataraman, L. H. Staib, and J. A. Onofrey, “Atlas-based semantic segmentation of prostate zones,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 570–579, Springer, 2022.
  16. B. B. Avants, C. L. Epstein, M. Grossman, and J. C. Gee, “Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain,” Medical image analysis, vol. 12, no. 1, pp. 26–41, 2008.
  17. W. Ding, L. Li, X. Zhuang, and L. Huang, “Cross-modality multi-atlas segmentation via deep registration and label fusion,” IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 7, pp. 3104–3115, 2022.
  18. W. Ding, L. Li, X. Zhuang, and L. Huang, “Cross-modality multi-atlas segmentation using deep neural networks,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 233–242, Springer, 2020.
  19. J. Wu, Q. Yang, and S. Zhou, “Latent shape image learning via disentangled representation for cross-sequence image registration and segmentation,” International Journal of Computer Assisted Radiology and Surgery, vol. 18, no. 4, pp. 621–628, 2023.
  20. L. Qiu and H. Ren, “Rsegnet: A joint learning framework for deformable registration and segmentation,” IEEE Transactions on Automation Science and Engineering, vol. 19, no. 3, pp. 2499–2513, 2021.
  21. J. Van Houtte, E. Audenaert, G. Zheng, and J. Sijbers, “Deep learning-based 2d/3d registration of an atlas to biplanar x-ray images,” International Journal of Computer Assisted Radiology and Surgery, vol. 17, no. 7, pp. 1333–1342, 2022.
  22. Y. Zhang, J. Wu, Y. Liu, Y. Chen, W. Chen, E. X. Wu, C. Li, and X. Tang, “A deep learning framework for pancreas segmentation with multi-atlas registration and 3d level-set,” Medical Image Analysis, vol. 68, p. 101884, 2021.
  23. Z. Ding and M. Niethammer, “Aladdin: Joint atlas building and diffeomorphic registration learning with pairwise alignment,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 20784–20793, 2022.
  24. R. Bajcsy and S. Kovačič, “Multiresolution elastic matching,” Computer vision, graphics, and image processing, vol. 46, no. 1, pp. 1–21, 1989.
  25. S. K. Warfield, K. H. Zou, and W. M. Wells, “Simultaneous truth and performance level estimation (staple): an algorithm for the validation of image segmentation,” IEEE transactions on medical imaging, vol. 23, no. 7, pp. 903–921, 2004.
  26. A. Rondinella, E. Crispino, F. Guarnera, O. Giudice, A. Ortis, G. Russo, C. Di Lorenzo, D. Maimone, F. Pappalardo, and S. Battiato, “Boosting multiple sclerosis lesion segmentation through attention mechanism,” Computers in Biology and Medicine, vol. 161, p. 107021, 2023.
  27. R. Azad, M. Heidari, M. Shariatnia, E. K. Aghdam, S. Karimijafarbigloo, E. Adeli, and D. Merhof, “Transdeeplab: Convolution-free transformer-based deeplab v3+ for medical image segmentation,” in International Workshop on PRedictive Intelligence In MEdicine, pp. 91–102, Springer, 2022.
  28. V. Badrinarayanan, A. Kendall, and R. Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE transactions on pattern analysis and machine intelligence, vol. 39, no. 12, pp. 2481–2495, 2017.
  29. H. Noh, S. Hong, and B. Han, “Learning deconvolution network for semantic segmentation,” in Proceedings of the IEEE international conference on computer vision, pp. 1520–1528, 2015.
  30. O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pp. 234–241, Springer, 2015.
  31. G. Balakrishnan, A. Zhao, M. R. Sabuncu, J. Guttag, and A. V. Dalca, “Voxelmorph: a learning framework for deformable medical image registration,” IEEE transactions on medical imaging, vol. 38, no. 8, pp. 1788–1800, 2019.
  32. B. B. Avants, N. Tustison, G. Song, et al., “Advanced normalization tools (ants),” Insight j, vol. 2, no. 365, pp. 1–35, 2009.
  33. Y. Hu, M. Modat, E. Gibson, W. Li, N. Ghavami, E. Bonmati, G. Wang, S. Bandula, C. M. Moore, M. Emberton, et al., “Weakly-supervised convolutional neural networks for multimodal image registration,” Medical image analysis, vol. 49, pp. 1–13, 2018.
  34. T. Estienne, M. Vakalopoulou, S. Christodoulidis, E. Battistela, M. Lerousseau, A. Carre, G. Klausner, R. Sun, C. Robert, S. Mougiakakou, et al., “U-resnet: Ultimate coupling of registration and segmentation with deep nets,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part III 22, pp. 310–319, Springer, 2019.
  35. R. A. Heckemann, J. V. Hajnal, P. Aljabar, D. Rueckert, and A. Hammers, “Automatic anatomical brain mri segmentation combining label propagation and decision fusion,” NeuroImage, vol. 33, no. 1, pp. 115–126, 2006.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now