Abstract
Deep convolutional networks have demonstrated state-of-the-art performance in a variety of medical image tasks, including segmentation. Taking advantage of images from different modalities has great clinical benefits. However, the generalization ability of deep networks on different modalities is challenging due to domain shift. In this work, we investigate the challenging unsupervised domain adaptation problem of cross-modality medical image segmentation. Cross-modality domain shift can be viewed as having two orthogonal components: appearance (modality) shift and content (anatomy) shift. Previous works using the popular adversarial training strategy emphasize the significant appearance/modality alignment caused by different physical principles while ignoring the content/anatomy alignment, which can be harmful for the downstream segmentation task. Here, we design a cross-modality segmentation pipeline, where self-supervision is introduced to achieve further semantic alignment specifically on the disentangled content space. In the self-supervision branch, in addition to rotation prediction, we also propose elastic transformation prediction as a new pretext task. We validate our model on cross-modality liver segmentation from CT to MR. Both quantitative and qualitative experimental results demonstrate that further semantic alignment through self-supervision can improve segmentation performance significantly, making the learned model more robust.
X. Li, D. Pak and N.C. Dvornek—Equal Contributions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Carlucci, F.M., D’Innocente, A., Bucci, S., Caputo, B., Tommasi, T.: Domain generalization by solving jigsaw puzzles. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2229–2238 (2019)
Chartsias, A., et al.: Disentangled representation learning in cardiac image analysis. Med. Image Anal. 58, 101535 (2019)
Chen, C., Dou, Q., Chen, H., Qin, J., Heng, P.A.: Synergistic image and feature adaptation: towards cross-modality domain adaptation for medical image segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 865–872 (2019)
Christ, P., Ettlinger, F., Grün, F., Lipkova, J., Kaissis, G.: LiTS-liver tumor segmentation challenge. ISBI and MICCAI (2017)
Dou, Q., et al.: Pnp-adanet: plug-and-play adversarial domain adaptation network at unpaired cross-modality cardiac segmentation. IEEE Access 7, 99065–99076 (2019)
Gidaris, S., Singh, P., Komodakis, N.: Unsupervised representation learning by predicting image rotations. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=S1v4N2l0-
Hoffman, J., et al.: Cycada: cycle-consistent adversarial domain adaptation. arXiv preprint arXiv:1711.03213 (2017)
Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1501–1510 (2017)
Huang, X., Liu, M.Y., Belongie, S., Kautz, J.: Multimodal unsupervised image-to-image translation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 172–189 (2018)
Li, W., Wang, Y., Cai, Y., Arnold, C., Zhao, E., Yuan, Y.: Semi-supervised rare disease detection using generative adversarial network. arXiv preprint arXiv:1812.00547 (2018)
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
Simard, P.Y., Steinkraus, D., Platt, J.C., et al.: Best practices for convolutional neural networks applied to visual document analysis. In: ICDAR, pp. 958–962 (2003)
Wang, M., Deng, W.: Deep visual domain adaptation: a survey. Neurocomputing 312, 135–153 (2018)
Yang, J., Dvornek, N.C., Zhang, F., Chapiro, J., Lin, M.D., Duncan, J.S.: Unsupervised domain adaptation via disentangled representations: application to cross-modality liver segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 255–263. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_29
Zhai, X., Oliver, A., Kolesnikov, A., Beyer, L.: S4l: self-supervised semi-supervised learning. arXiv preprint arXiv:1905.03670 (2019)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
Acknowledgement
This work was supported by NIH Grant 5R01 CA206180
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Yang, J. et al. (2020). Cross-Modality Segmentation by Self-supervised Semantic Alignment in Disentangled Content Space. In: Albarqouni, S., et al. Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning. DART DCL 2020 2020. Lecture Notes in Computer Science(), vol 12444. Springer, Cham. https://doi.org/10.1007/978-3-030-60548-3_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-60548-3_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60547-6
Online ISBN: 978-3-030-60548-3
eBook Packages: Computer ScienceComputer Science (R0)