Abstract
Recent unsupervised domain adaptation methods in medical image segmentation adopt centroid/prototypical contrastive learning (CL) to match the source and target features for their excellent ability of representation learning and semantic feature alignment. Of these CL methods, most works extract features with a binary mask generated by similarity measure or thresholding the prediction. However, this hard-threshold (HT) strategy may induce sparse features and incorrect label assignments. Conversely, while the soft-labeling technique has proven effective in addressing the limitations of the HT strategy by assigning importance factors to pixel features, it remains unexplored in CL algorithms. Thus, in this work, we present a novel CL approach leveraging soft pseudo labels for category-wise target centroid generation, complemented by a reversed Monte Carlo method to achieve a more compact target feature space. Additionally, we propose a centroid norm regularizer as an extra magnitude constraint to bolster the model’s robustness. Extensive experiments and ablation studies on two cardiac data sets underscore the effectiveness of each component and reveal a significant enhancement in segmentation results in Dice Similarity Score and Hausdorff Distance 95 compared with a wide range of state-of-the-art methods.
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
Cai, J., Xia, Y., Yang, D., Xu, D., Yang, L., Roth, H.: End-to-end adversarial shape learning for abdomen organ deep segmentation. In: Suk, H.-I., Liu, M., Yan, P., Lian, C. (eds.) MLMI 2019. LNCS, vol. 11861, pp. 124–132. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32692-0_15
Chaitanya, K., Erdil, E., Karani, N., Konukoglu, E.: Contrastive learning of global and local features for medical image segmentation with limited annotations. In: Advances in Neural Information Processing Systems, vol. 33, pp. 12546–12558 (2020)
Haq, M.M., Huang, J.: Adversarial domain adaptation for cell segmentation. In: Medical Imaging with Deep Learning, pp. 277–287. PMLR (2020)
Harrison, R.L.: Introduction to Monte Carlo simulation. In: AIP Conference Proceedings, vol. 1204, pp. 17–21. American Institute of Physics (2010)
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729–9738 (2020)
Kuang, S., et al.: MSCDA: multi-level semantic-guided contrast improves unsupervised domain adaptation for breast MRI segmentation in small datasets. Neural Netw. 165, 119–134 (2023)
Lee, G., Eom, C., Lee, W., Park, H., Ham, B.: Bi-directional contrastive learning for domain adaptive semantic segmentation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022, Part XXX. LNCS, vol. 13690, pp. 38–55. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20056-4_3
Liang, C., Cheng, B., Xiao, B., Dong, Y., Chen, J.: Multilevel heterogeneous domain adaptation method for remote sensing image segmentation. IEEE Trans. Geosci. Remote Sens. 61, 1–16 (2023)
Liu, Z., Zhu, Z., Zheng, S., Liu, Y., Zhou, J., Zhao, Y.: Margin preserving self-paced contrastive learning towards domain adaptation for medical image segmentation. IEEE J. Biomed. Health Inform. 26(2), 638–647 (2022)
Marsden, R.A., Bartler, A., Döbler, M., Yang, B.: Contrastive learning and self-training for unsupervised domain adaptation in semantic segmentation. In: 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2022)
Mugnai, D., Pernici, F., Turchini, F., Del Bimbo, A.: Soft pseudo-labeling semi-supervised learning applied to fine-grained visual classification. In: Del Bimbo, A., et al. (eds.) ICPR 2021. LNCS, vol. 12664, pp. 102–110. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68799-1_8
Müller, R., Kornblith, S., Hinton, G.E.: When does label smoothing help? Advances in Neural Information Processing Systems, vol. 32 (2019)
Tanaka, D., Ikami, D., Yamasaki, T., Aizawa, K.: Joint optimization framework for learning with noisy labels. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5552–5560 (2018)
Thulasidasan, S., Chennupati, G., Bilmes, J.A., Bhattacharya, T., Michalak, S.: On mixup training: improved calibration and predictive uncertainty for deep neural networks. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
Tsai, Y., Hung, W., Schulter, S., Sohn, K., Yang, M., Chandraker, M.: Learning to adapt structured output space for semantic segmentation. In: CVPR, pp. 7472–7481 (2018)
Vesal, S., Gu, M., Kosti, R., Maier, A., Ravikumar, N.: Adapt everywhere: unsupervised adaptation of point-clouds and entropy minimization for multi-modal cardiac image segmentation. IEEE Trans. Med. Imaging 40(7), 1838–1851 (2021)
Vu, T.H., Jain, H., Bucher, M., Cord, M., Pérez, P.: Advent: adversarial entropy minimization for domain adaptation in semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2517–2526 (2019)
Wang, S., Yu, L., Li, K., Yang, X., Fu, C.-W., Heng, P.-A.: Boundary and entropy-driven adversarial learning for fundus image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 102–110. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_12
Wang, Y., et al.: 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, F., Zhuang, X.: CF distance: a new domain discrepancy metric and application to explicit domain adaptation for cross-modality cardiac image segmentation. IEEE Trans. Med. Imaging 39(12), 4274–4285 (2020)
Xie, B., Li, S., Li, M., Liu, C.H., Huang, G., Wang, G.: Sepico: semantic-guided pixel contrast for domain adaptive semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 45(7), 9004–9021 (2023)
Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)
Zhao, X., et al.: Contrastive learning for label efficient semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10623–10633 (2021)
Zhuang, X.: Multivariate mixture model for myocardial segmentation combining multi-source images. IEEE Trans. Pattern Anal. Mach. Intell. 41(12), 2933–2946 (2018)
Zhuang, X., et al.: Evaluation of algorithms for multi-modality whole heart segmentation: an open-access grand challenge. Med. Image Anal. 58, 101537 (2019)
Zhuang, X., Shen, J.: Multi-scale patch and multi-modality atlases for whole heart segmentation of MRI. Med. Image Anal. 31, 77–87 (2016)
Zou, Y., Yu, Z., Liu, X., Kumar, B., Wang, J.: Confidence regularized self-training. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5982–5991 (2019)
Acknowledgments
The work is supported by the European Research Council (ERC Grant No. 810316) and HPC resources are provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-UniversitätErlangen-Nürnberg. The hardware is funded by the German Research Foundation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Disclosure of Interests
The authors have no competing interests to declare that are relevant to the content of this article.
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Gu, M. et al. (2024). Unsupervised Domain Adaptation Using Soft-Labeled Contrastive Learning with Reversed Monte Carlo Method for Cardiac Image Segmentation. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15009. Springer, Cham. https://doi.org/10.1007/978-3-031-72114-4_65
Download citation
DOI: https://doi.org/10.1007/978-3-031-72114-4_65
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-72113-7
Online ISBN: 978-3-031-72114-4
eBook Packages: Computer ScienceComputer Science (R0)
