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DUDA: Deep Unsupervised Domain Adaptation Learning for Multi-sequence Cardiac MR Image Segmentation

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Book cover Pattern Recognition and Computer Vision (PRCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12305))

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Abstract

Automatic segmentation of ventricles and myocardium in late gadolinium-enhanced (LGE) cardiac images is an important assessment for the clinical diagnosis of myocardial infarction. The most segmentation methods rely on the annotation of ventricles and myocardium. However, pathological myocardium in LGE images makes its annotation difficult to be obtained. In this paper, we propose a new deep unsupervised domain adaptation (DUDA) framework, which can effectively solve the segmentation problem of LGE images without any annotations through cross domain based adversarial learning. A CycleGAN network is used to construct the LGE image training set, meanwhile, a encoder and classier is used as a segmentor to generate the prediction mask. To keep our domain adaption model segmentation results consistent, a new loss function named cross-domain consistency loss is introduced. We tested our method on the dataset of the MICCAI2019 Multi-Sequence cardiac MR image Segmentation Challenge. The average dice of the myocardium, left ventricle, and right ventricle segmentation is 0.762, 0.857, and 0.841, respectively. Compared with other domain adaptation methods, our method can well segment myocardium and right ventricle structures in LGE images. Experimental results prove the superiority of our proposed framework and it can effectively help doctors diagnose myocardial infarction faster without any manual intervention in the clinic.

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Acknowledgment

This work was supported in part by the National Science Foundation of China (61702003), and in part by the Anhui Provincial Natural Science Foundation (1708085QF143, 1808085MF175). The authors also thank all the anonymous reviewers for their valuable comments and suggestions, which were helpful for improving the quality of the paper.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei, China

    Xiuquan Du

  2. School of Computer Science and Technology, Anhui University, Hefei, China

    Yueguo Liu & Xiuquan Du

Authors
  1. Yueguo Liu
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  2. Xiuquan Du
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Corresponding author

Correspondence to Xiuquan Du .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  3. Dalian University of Technology, Dalian, China

    Huchuan Lu

  4. Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  5. Chinese Academy of Sciences, Beijing, China

    Chenglin Liu

  6. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Peking University, Beijing, China

    Hongbin Zha

  8. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

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Liu, Y., Du, X. (2020). DUDA: Deep Unsupervised Domain Adaptation Learning for Multi-sequence Cardiac MR Image Segmentation. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12305. Springer, Cham. https://doi.org/10.1007/978-3-030-60633-6_42

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  • DOI: https://doi.org/10.1007/978-3-030-60633-6_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60632-9

  • Online ISBN: 978-3-030-60633-6

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