Abstract
Semi-supervised learning (SSL) has emerged as a promising approach for medical image segmentation, while its capacity has still been limited by the difficulty in quantifying the reliability of unlabeled data and the lack of effective strategies for exploiting unlabeled regions with ambiguous predictions. To address these issues, we propose an Uncertainty-informed Prototype Consistency Learning (UPCoL) framework, which learns fused prototype representations from labeled and unlabeled data judiciously by incorporating an entropy-based uncertainty mask. The consistency constraint enforced on prototypes leads to a more discriminative and compact prototype representation for each class, thus optimizing the distribution of hidden embeddings. We experiment with two benchmark datasets of two-class semi-supervised segmentation, left atrium and pancreas, as well as a three-class multi-center dataset of type B aortic dissection. For all three datasets, UPCoL outperforms the state-of-the-art SSL methods, demonstrating the efficacy of the uncertainty-informed prototype learning strategy (Code is available at https://github.com/VivienLu/UPCoL).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bai, Wenjia, et al.: Semi-supervised learning for network-based cardiac MR image segmentation. In: Descoteaux, Maxime, Maier-Hein, Lena, Franz, Alfred, Jannin, Pierre, Collins, D. Louis., Duchesne, Simon (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 253–260. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66185-8_29
Cao, L., et al.: Fully automatic segmentation of type b aortic dissection from CTA images enabled by deep learning. Europ. J. Radiol. 121, 108713 (2019)
Dong, N., **ng, E.P.: Few-shot semantic segmentation with prototype learning. In: BMVC, vol. 3 (2018)
Fantazzini, A., et al.: 3d automatic segmentation of aortic computed tomography angiography combining multi-view 2d convolutional neural networks. Cardiovascular Eng. Technol. 11, 576–586 (2020)
Hang, Wenlong, et al.: Local and global structure-aware entropy regularized mean teacher model for 3D left atrium segmentation. In: Martel, Anne L.., Abolmaesumi, Purang, Stoyanov, Danail, Mateus, Diana, Zuluaga, Maria A.., Zhou, S. Kevin., Racoceanu, Daniel, Joskowicz, Leo (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 562–571. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59710-8_55
Lei, T., Zhang, D., Du, X., Wang, X., Wan, Y., Nandi, A.K.: Semi-supervised medical image segmentation using adversarial consistency learning and dynamic convolution network. IEEE Trans. Med. Imaging (2022)
Li, Shuailin, Zhang, Chuyu, He, Xuming: Shape-aware semi-supervised 3d semantic segmentation for medical images. In: Martel, Anne L.., Abolmaesumi, Purang, Stoyanov, Danail, Mateus, Diana, Zuluaga, Maria A.., Zhou, S. Kevin., Racoceanu, Daniel, Joskowicz, Leo (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 552–561. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59710-8_54
Li, X., Yu, L., Chen, H., Fu, C.W., **ng, L., Heng, P.A.: Transformation-consistent self-ensembling model for semisupervised medical image segmentation. IEEE Trans. Neural Networks Learn. Syst. 32(2), 523–534 (2020)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Luo, X., Chen, J., Song, T., Wang, G.: Semi-supervised medical image segmentation through dual-task consistency. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 35, pp. 8801–8809 (2021)
Luo, X., Liao, W., Chen, J., Song, T., Chen, Y., Zhang, S., Chen, N., Wang, G., Zhang, S.: Efficient semi-supervised gross target volume of nasopharyngeal carcinoma segmentation via uncertainty rectified pyramid consistency. In: MICCAI 2021. pp. 318–329. Springer (2021)
Luo, X., Wang, G., Liao, W., Chen, J., Song, T., Chen, Y., Zhang, S., Metaxas, D.N., Zhang, S.: Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency. Medical Image Analysis 80, 102517 (2022)
Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV). pp. 565–571. Ieee (2016)
Nie, D., Gao, Y., Wang, L., Shen, D.: Asdnet: attention based semi-supervised deep networks for medical image segmentation. In: MICCAI 2018. pp. 370–378. Springer (2018)
Shi, Y., Zhang, J., Ling, T., Lu, J., Zheng, Y., Yu, Q., Qi, L., Gao, Y.: Inconsistency-aware uncertainty estimation for semi-supervised medical image segmentation. IEEE transactions on medical imaging 41(3), 608–620 (2021)
Tarvainen, A., Valpola, H.: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Advances in neural information processing systems 30 (2017)
Wang, K., Liew, J.H., Zou, Y., Zhou, D., Feng, J.: Panet: Few-shot image semantic segmentation with prototype alignment. In: proceedings of the IEEE/CVF international conference on computer vision. pp. 9197–9206 (2019)
Wang, Y., Wang, H., Shen, Y., Fei, J., Li, W., **, G., Wu, L., Zhao, R., Le, X.: Semi-supervised semantic segmentation using unreliable pseudo-labels. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4248–4257 (2022)
Wu, L., Fang, L., He, X., He, M., Ma, J., Zhong, Z.: Querying labeled for unlabeled: Cross-image semantic consistency guided semi-supervised semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023)
Wu, Y., Ge, Z., Zhang, D., Xu, M., Zhang, L., **a, Y., Cai, J.: Mutual consistency learning for semi-supervised medical image segmentation. Medical Image Analysis 81, 102530 (2022)
Wu, Y., Xu, M., Ge, Z., Cai, J., Zhang, L.: Semi-supervised left atrium segmentation with mutual consistency training. In: MICCAI 2021. pp. 297–306. Springer (2021)
**ang, J., Qiu, P., Yang, Y.: Fussnet: Fusing two sources of uncertainty for semi-supervised medical image segmentation. In: MICCAI 2022. pp. 481–491. Springer (2022)
**e, Q., Luong, M.T., Hovy, E., Le, Q.V.: Self-training with noisy student improves imagenet classification. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10687–10698 (2020)
Xu, Z., Wang, Y., Lu, D., Yu, L., Yan, J., Luo, J., Ma, K., Zheng, Y., Tong, R.K.y.: All-around real label supervision: Cyclic prototype consistency learning for semi-supervised medical image segmentation. IEEE Journal of Biomedical and Health Informatics 26(7), 3174–3184 (2022)
Yao, Z., **e, W., Zhang, J., Dong, Y., Qiu, H., Yuan, H., Jia, Q., Wang, T., Shi, Y., Zhuang, J., et al.: Imagetbad: A 3d computed tomography angiography image dataset for automatic segmentation of type-b aortic dissection. Frontiers in Physiology p. 1611 (2021)
You, C., Zhou, Y., Zhao, R., Staib, L., Duncan, J.S.: Simcvd: Simple contrastive voxel-wise representation distillation for semi-supervised medical image segmentation. IEEE Transactions on Medical Imaging 41(9), 2228–2237 (2022)
Yu, L., Wang, S., Li, X., Fu, C.W., Heng, P.A.: Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation. In: MICCAI 2019. pp. 605–613. Springer (2019)
Zeng, X., Huang, R., Zhong, Y., Sun, D., Han, C., Lin, D., Ni, D., Wang, Y.: Reciprocal learning for semi-supervised segmentation. In: MICCAI 2021. pp. 352–361. Springer (2021)
Zhang, X., Wei, Y., Yang, Y., Huang, T.S.: Sg-one: Similarity guidance network for one-shot semantic segmentation. IEEE transactions on cybernetics 50(9), 3855–3865 (2020)
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Nos. 61972251 and 62272300).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Lu, W. et al. (2023). UPCoL: Uncertainty-Informed Prototype Consistency Learning for Semi-supervised Medical Image Segmentation. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14223. Springer, Cham. https://doi.org/10.1007/978-3-031-43901-8_63
Download citation
DOI: https://doi.org/10.1007/978-3-031-43901-8_63
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43900-1
Online ISBN: 978-3-031-43901-8
eBook Packages: Computer ScienceComputer Science (R0)
