Abstract
Semi-supervised learning has been recently employed to solve problems from medical image segmentation due to challenges in acquiring sufficient manual annotations, which is an important prerequisite for building high-performance deep learning methods. Since unlabeled data is generally abundant, most existing semi-supervised approaches focus on how to make full use of both limited labeled data and abundant unlabeled data. In this paper, we propose a novel semi-supervised strategy called reciprocal learning for medical image segmentation, which can be easily integrated into any CNN architecture. Concretely, the reciprocal learning works by having a pair of networks, one as a student and one as a teacher. The student model learns from pseudo label generated by the teacher. Furthermore, the teacher updates its parameters autonomously according to the reciprocal feedback signal of how well student performs on the labeled set. Extensive experiments on two public datasets show that our method outperforms current state-of-the-art semi-supervised segmentation methods, demonstrating the potential of our strategy for the challenging semi-supervised problems. The code is publicly available at https://github.com/XYZach/RLSSS.
X. Zeng, R. Huang and Y. Zhong—Contribute equally to this work.
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
Notes
- 1.
This study mainly focused on the challenging problem of semi-supervised learning for insufficient annotations. Several semi-supervised segmentation studies used cropped images for validations, e.g., UAMT [16] used cropped left atrium images, and [8] used cropped pancreas images. We followed their experimental settings.
References
Bai, W., et al.: Semi-supervised learning for network-based cardiac MR image segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 253–260. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66185-8_29
Hang, W., et al.: Local and global structure-aware entropy regularized mean teacher model for 3D left atrium segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 562–571. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59710-8_55
Laine, S., Aila, T.: Temporal ensembling for semi-supervised learning. arXiv preprint arXiv:1610.02242 (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
Li, X., Yu, L., Chen, H., Fu, C.W., Xing, L., Heng, P.A.: Transformation-consistent self-ensembling model for semisupervised medical image segmentation. TNNLS 32(2), 523–534 (2020)
Liu, H., Simonyan, K., Yang, Y.: DARTS: differentiable architecture search. In: ICLR (2018)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431–3440 (2015)
Luo, X., Chen, J., Song, T., Wang, G.: Semi-supervised medical image segmentation through dual-task consistency. In: AAAI Conference on Artificial Intelligence (2021)
Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 3DV, pp. 565–571. IEEE (2016)
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
Roth, H.R., et al.: DeepOrgan: multi-level deep convolutional networks for automated pancreas segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 556–564. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24553-9_68
Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: NIPS, pp. 1195–1204 (2017)
Williams, R.J.: Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach. Learn. 8(3–4), 229–256 (1992). https://doi.org/10.1007/BF00992696
Xia, Y., et al.: 3D semi-supervised learning with uncertainty-aware multi-view co-training. In: WACV, pp. 3646–3655 (2020)
Xiong, Z., et al.: A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging. Med. Image Anal. 67, 101832 (2021)
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
Acknowledgements
This work was supported in part by the National Key R&D Program of China (No. 2019YFC0118300), in part by the National Natural Science Foundation of China under Grants 62071305, 61701312 and 81971631, in part by the Guangdong Basic and Applied Basic Research Foundation (2019A1515010847), in part by the Medical Science and Technology Foundation of Guangdong Province (B2019046), in part by the Natural Science Foundation of Shenzhen University (No. 860-000002110129), and in part by the Shenzhen Peacock Plan (No. KQTD2016053112051497).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Zeng, X. et al. (2021). Reciprocal Learning for Semi-supervised Segmentation. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12902. Springer, Cham. https://doi.org/10.1007/978-3-030-87196-3_33
Download citation
DOI: https://doi.org/10.1007/978-3-030-87196-3_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87195-6
Online ISBN: 978-3-030-87196-3
eBook Packages: Computer ScienceComputer Science (R0)
