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Cross-Domain Transfer Learning for Vessel Segmentation in Computed Tomographic Coronary Angiographic Images

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Image and Graphics (ICIG 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12889))

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Abstract

Segmenting coronary arteries in computed tomographic angiography images is an essential procedure for coronary artery disease diagnosis. However, it still remains challenging due to the insufficient annotation data for supervised deep learning methods. To solve this problem, we propose a novel cross-domain transfer learning network to adaptively transfer knowledge learned from public liver vessel dataset for coronary artery segmentation. The signed distance map learning task is joined to enforce the network to transfer tubular structure knowledge from the liver vessel. Moreover, an adaptive feature-selection module is used to determine the optimal fine-tune strategy for every target sample. We conduct ablation experiments to demonstrate the effectiveness of the auxiliary task and module. We also compare the proposed method with other state-of-the-art transfer learning and segmentation methods. Results showed that our method achieve the best performance on accurate coronary artery segmentation. Our method achieves the best Dice score of 81.60%, an improvement of at least 1% with respect to other methods.

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Acknowledgements

This work was supported by the National Science Foundation Program of China [62071048, 61901031, 61971040].

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Authors and Affiliations

  1. Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China

    Ruirui An, Tao Han, Danni Ai, Yongtian Wang & Jian Yang

  2. Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China

    Yining Wang

Authors
  1. Ruirui An
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  2. Tao Han
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  3. Yining Wang
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  4. Danni Ai
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  5. Yongtian Wang
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  6. Jian Yang
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Corresponding author

Correspondence to Danni Ai .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Tsinghua University, Beijing, China

    Shi-Min Hu

  3. Tampere University, Tampere, Finland

    Moncef Gabbouj

  4. Zhejiang University, Hangzhou, China

    Kun Zhou

  5. Technion – Israel Institute of Technology, Haifa, Israel

    Michael Elad

  6. Tsinghua University, Beijing, China

    Kun Xu

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An, R., Han, T., Wang, Y., Ai, D., Wang, Y., Yang, J. (2021). Cross-Domain Transfer Learning for Vessel Segmentation in Computed Tomographic Coronary Angiographic Images. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12889. Springer, Cham. https://doi.org/10.1007/978-3-030-87358-5_46

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  • DOI: https://doi.org/10.1007/978-3-030-87358-5_46

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  • Online ISBN: 978-3-030-87358-5

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