Abstract
In semi-supervised medical image segmentation, the limited amount of labeled data available for training is often insufficient to learn the variability and complexity of target regions. To overcome these challenges, we propose a novel framework based on cross-model pseudo-supervision that generates anatomically plausible predictions using shape awareness and local context constraints. Our framework consists of two parallel networks, a shape-aware network and a shape-agnostic network, which provide pseudo-labels to each other for using unlabeled data effectively. The shape-aware network implicitly captures information on the shape of target regions by adding the prediction of the other network as input. On the other hand, the shape-agnostic network leverages Monte-Carlo dropout uncertainty estimation to generate reliable pseudo-labels to the other network. The proposed framework also comprises a new loss function that enables the network to learn the local context of the segmentation, thus improving the overall segmentation accuracy. Experiments on two publicly-available datasets show that our method outperforms state-of-the-art approaches for semi-supervised segmentation and better preserves anatomical morphology compared to these approaches. Code is available at https://github.com/igip-liu/SLC-Net.
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References
Bernard, O., et al.: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans. Med. Imaging 37(11), 2514–2525 (2018)
Chaitanya, K., Karani, N., Baumgartner, C.F., Becker, A., Donati, O., Konukoglu, E.: Semi-supervised and task-driven data augmentation. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 29–41. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20351-1_3
Chen, L., Zhang, W., Wu, Y., Strauch, M., Merhof, D.: Semi-supervised instance segmentation with a learned shape prior. In: Cardoso, J., et al. (eds.) IMIMIC/MIL3ID/LABELS -2020. LNCS, vol. 12446, pp. 94–102. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61166-8_10
Chen, X., Yuan, Y., Zeng, G., Wang, J.: Semi-supervised semantic segmentation with cross pseudo supervision. In: Computer Vision Foundation, CVPR 2021, pp. 2613–2622. IEEE (2021)
Cui, W., et al.: Semi-supervised brain lesion segmentation with an adapted mean teacher model. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 554–565. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20351-1_43
Hu, X., Zeng, D., Xu, X., Shi, Y.: Semi-supervised contrastive learning for label-efficient medical image segmentation. In: de Bruijne, M., et al. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part II, pp. 481–490. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87196-3_45
Huang, H., et al.: 3D graph-S2Net: shape-aware self-ensembling network for semi-supervised segmentation with bilateral graph convolution. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 416–427. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87196-3_39
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. Meth. 18(2), 203–211 (2021)
Li, K., Hariharan, B., Malik, J.: Iterative instance segmentation. In: CVPR 2016, pp. 3659–3667. IEEE Computer Society (2016)
Li, S., Zhang, C., He, X.: Shape-aware semi-supervised 3D semantic segmentation for medical images. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 552–561. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59710-8_54
Litjens, G., et al.: Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge. Med. Image Anal. 18(2), 359–373 (2014)
Luo, X.: SSL4MIS (2020). https://github.com/HiLab-git/SSL4MIS
Luo, X., Chen, J., Song, T., Wang, G.: Semi-supervised medical image segmentation through dual-task consistency. In: AAAI 2021, pp. 8801–8809. AAAI Press (2021)
Luo, X., Hu, M., Song, T., Wang, G., Zhang, S.: Semi-supervised medical image segmentation via cross teaching between CNN and transformer. CoRR abs/2112.04894 (2021)
Luo, X., et al.: Efficient semi-supervised gross target volume of nasopharyngeal carcinoma segmentation via uncertainty rectified pyramid consistency. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 318–329. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87196-3_30
Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 4th International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)
Peng, J., Wang, P., Desrosiers, C., Pedersoli, M.: Self-paced contrastive learning for semi-supervised medical image segmentation with meta-labels. In: Advances in Neural Information Processing Systems 34 (2021)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Guyon, I., et al. (eds.) NIPS 2017, pp. 1195–1204 (2017)
Verma, V., et al.: Interpolation consistency training for semi-supervised learning. Neural Netw. 145, 90–106 (2022)
Vu, T., Jain, H., Bucher, M., Cord, M., Pérez, P.: ADVENT: adversarial entropy minimization for domain adaptation in semantic segmentation. In: CVPR 2019, pp. 2517–2526. Computer Vision Foundation/IEEE (2019)
Wang, G., et al.: Semi-supervised segmentation of radiation-induced pulmonary fibrosis from lung CT scans with multi-scale guided dense attention. IEEE Trans. Med. Imaging 41, 531–542 (2021)
Wang, K., et al.: Tripled-uncertainty guided mean teacher model for semi-supervised medical image segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 450–460. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87196-3_42
Wang, P., Peng, J., Pedersoli, M., Zhou, Y., Zhang, C., Desrosiers, C.: Context-aware virtual adversarial training for anatomically-plausible segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 304–314. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87193-2_29
Wang, P., Peng, J., Pedersoli, M., Zhou, Y., Zhang, C., Desrosiers, C.: Self-paced and self-consistent co-training for semi-supervised image segmentation. Med. Image Anal. 73, 102146 (2021)
Xia, Y., et al.: Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation. Med. Image Anal. 65, 101766 (2020)
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: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 605–613. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_67
Zhang, Y., Yang, L., Chen, J., Fredericksen, M., Hughes, D.P., Chen, D.Z.: Deep adversarial networks for biomedical image segmentation utilizing unannotated images. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 408–416. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_47
Acknowledgement
This work is supported by the National Key R &D Plan on Strategic International Scientific and Technological Innovation Cooperation Special Project (No. 2021YFE0203800), the NSFC-Zhejiang Joint Fund of the Integration of Informatization and Industrialization (No. U1909210), the National Natural Science Foundation of China under Grant (No. 62172257, 61902217), the Natural Science Foundation of Shandong Province (ZR2019BF043).
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Liu, J., Desrosiers, C., Zhou, Y. (2022). Semi-supervised Medical Image Segmentation Using Cross-Model Pseudo-Supervision with Shape Awareness and Local Context Constraints. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13438. Springer, Cham. https://doi.org/10.1007/978-3-031-16452-1_14
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