Abstract
Numerous unlabeled data is useful for supervised medical image segmentation, if the labeled data is limited. To leverage all the unlabeled images for efficient abdominal organ segmentation, we developed semi-supervised framework with cross supervision using siamese network, i.e., SemiSeg-CSSN. Cross supervision enables the two networks to optimize the network using pseudo-labels generated by the other. Moreover, we applied the cascade strategy for the task because of the large and uncertain locations of the abdomen regions. To validate the effects of unlabeled data, we employed an unlabeled image filtering strategy to select the unlabeled image and their pseudo label images with low uncertainty. On the FLARE2022 validation cases, with the help of unlabeled data, our method obtained the average dice similarity coefficient (DSC) of 77.7% and average normalized surface distance (NSD) of 82.0%, which is better than the supervised method. The average running time is 12.9 s per case in inference phase and maximum used GPU memory is 2052 MB.
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References
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pp. 92–100 (1998)
Chen, L.-C., Lopes, R.G., Cheng, B., Collins, M.D., Cubuk, E.D., Zoph, B., Adam, H., Shlens, J.: Naive-student: leveraging semi-supervised learning in video sequences for urban scene segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 695–714. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58545-7_40
Chen, X., Yuan, Y., Zeng, G., Wang, J.: Semi-supervised semantic segmentation with cross pseudo supervision. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2613–2622 (2021)
Chen, Y., Mancini, M., Zhu, X., Akata, Z.: Semi-supervised and unsupervised deep visual learning: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 2022, 1–23 (2022). https://doi.org/10.1109/TPAMI.2022.3201576
Clark, K., et al.: The cancer imaging archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26(6), 1045–1057 (2013)
Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3213–3223 (2016)
Everingham, M., Eslami, S., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes challenge: A retrospective. Int. J. Comput. Vision 111(1), 98–136 (2015)
Fralick, S.: Learning to recognize patterns without a teacher. IEEE Trans. Inf. Theory 13(1), 57–64 (1967)
Heller, N., et al.: The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: results of the kits19 challenge. Med. Image Anal. 67, 101821 (2021)
Heller, N., et al.: An international challenge to use artificial intelligence to define the state-of-the-art in kidney and kidney tumor segmentation in ct imaging. Proc. Am. Soc. Clin. Oncol. 38(6), 626–626 (2020)
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: NNU-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)
Ma, J., et al.: Fast and low-GPU-memory abdomen CT organ segmentation: the flare challenge. Med. Image Anal. 82, 102616 (2022). https://doi.org/10.1016/j.media.2022.102616
Ma, J., et al.: Abdomenct-1k: is abdominal organ segmentation a solved problem? IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 6695–6714 (2022)
Mittal, S., Tatarchenko, M., Brox, T.: Semi-supervised semantic segmentation with high-and low-level consistency. IEEE Trans. Pattern Anal. Mach. Intell. 43(4), 1369–1379 (2019)
Scudder, H.: Probability of error of some adaptive pattern-recognition machines. IEEE Trans. Inf. Theory 11(3), 363–371 (1965)
Simpson, A.L., et al.: A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv preprint arXiv:1902.09063 (2019)
Yang, L., Zhuo, W., Qi, L., Shi, Y., Gao, Y.: St++: make self-training work better for semi-supervised semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4268–4277 (2022),
Zhang, F., Wang, Y., Yang, H.: Efficient context-aware network for abdominal multi-organ segmentation. arXiv preprint arXiv:2109.10601 (2021)
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The authors of this paper declare that the segmentation method they implemented for participation in the FLARE 2022 challenge has not used any pre-trained models nor additional datasets other than those provided by the organizers.
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Jia, D. (2022). Semi-supervised Multi-organ Segmentation with Cross Supervision Using Siamese Network. In: Ma, J., Wang, B. (eds) Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation. FLARE 2022. Lecture Notes in Computer Science, vol 13816. Springer, Cham. https://doi.org/10.1007/978-3-031-23911-3_26
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