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TSP-UDANet: two-stage progressive unsupervised domain adaptation network for automated cross-modality cardiac segmentation

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Abstract

Accurate segmentation of cardiac anatomy is a prerequisite for the diagnosis of cardiovascular disease. However, due to differences in imaging modalities and imaging devices, known as domain shift, the segmentation performance of deep learning models lacks reliability. In this paper, we propose a two-stage progressive unsupervised domain adaptation network (TSP-UDANet) to reduce domain shift when segmenting cardiac images from various sources. We alleviate the domain shift between the feature distribution of the source and target domains by introducing an intermediate domain as a bridge. The TSP-UDANet consists of three sub-networks: a style transfer sub-network, a segmentation sub-network, and a self-training sub-network. We conduct cooperative alignment of different domains at image level, feature level, and output level. Specifically, we transform the appearance of images across domains and enhance domain invariance by adversarial learning in multiple aspects to achieve unsupervised segmentation of the target modality. We validate the TSP-UDANet on the MMWHS (unpaired MRI and CT images), MS-CMRSeg (cross-modality MRI images), and M&Ms (cross-vendor MRI images) datasets. The experimental results demonstrate excellent segmentation performance and generalizability for unlabeled target modality images.

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Data availability

The data that support the findings of this study are publicly available in the following links: MMWHS https://zmiclab.github.io/projects/mmwhs/. MS-CMRSeg https://zmiclab.github.io/projects/mscmrseg19/. M&Ms https://www.ub.edu/mnms/.

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Acknowledgements

This work is supported by the National Key Research and Development Program of China (2020YFC2004400), the National Natural Science Foundation of China (No. 61773110), the Fundamental Research Funds for the Central Universities (No. N2119008), Natural Science Foundation of LiaoNing Province (General Program) (No.2021-MS-087) and Open Grant by National Health Commission Key Laboratory of Assisted Circulation (Sun Yat-sen University) (No. cvclab201901). The authors would also like to thank the editor and reviewers for their valuable advice which has helped to improve the article.

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Authors and Affiliations

  1. College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China

    Yonghui Wang, Yifan Zhang, Lisheng Xu, Shouliang Qi, Yudong Yao, Wei Qian & Lin Qi

  2. Engineering Research Center of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang, 110169, China

    Lisheng Xu, Shouliang Qi & Lin Qi

  3. Key Laboratory of Medical Image Computing, Ministry of Education, Northeastern University, Shenyang, 110169, China

    Lisheng Xu, Shouliang Qi & Lin Qi

  4. Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA

    Yudong Yao

  5. Department of Electric and Computer Engineering, College of Engineering, University of Texas, El Paso, USA

    Wei Qian

  6. Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, E1 2AD, UK

    Stephen E. Greenwald

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  1. Yonghui Wang
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Wang, Y., Zhang, Y., Xu, L. et al. TSP-UDANet: two-stage progressive unsupervised domain adaptation network for automated cross-modality cardiac segmentation. Neural Comput & Applic 35, 22189–22207 (2023). https://doi.org/10.1007/s00521-023-08939-6

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