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Cross-Adversarial Local Distribution Regularization for Semi-supervised Medical Image Segmentation

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 (MICCAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14220))

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Abstract

Medical semi-supervised segmentation is a technique where a model is trained to segment objects of interest in medical images with limited annotated data. Existing semi-supervised segmentation methods are usually based on the smoothness assumption. This assumption implies that the model output distributions of two similar data samples are encouraged to be invariant. In other words, the smoothness assumption states that similar samples (e.g., adding small perturbations to an image) should have similar outputs. In this paper, we introduce a novel cross-adversarial local distribution (Cross-ALD) regularization to further enhance the smoothness assumption for semi-supervised medical image segmentation task. We conducted comprehensive experiments that the Cross-ALD archives state-of-the-art performance against many recent methods on the public LA and ACDC datasets.

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Notes

  1. 1.

    The Cross-ALD implementation in https://github.com/PotatoThanh/Cross-adversarial-local-distribution-regularization.

  2. 2.

    A sample generated by adding perturbations toward the adversarial direction.

  3. 3.

    https://github.com/ycwu1997/SS-Net.

  4. 4.

    https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html.

  5. 5.

    http://atriaseg2018.cardiacatlas.org.

  6. 6.

    https://github.com/ycwu1997/SS-Net.

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Acknowledgements

This work was partially supported by the Australian Defence Science and Technology (DST) Group under the Next Generation Technology Fund (NGTF) scheme. Dinh Phung further gratefully acknowledges the partial support from the Australian Research Council, project ARC DP230101176.

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Authors and Affiliations

  1. Monash University, Melbourne, Australia

    Thanh Nguyen-Duc, Trung Le, Roland Bammer, Jianfei Cai & Dinh Phung

  2. CSIRO’s Data61, Melbourne, Australia

    He Zhao

Authors
  1. Thanh Nguyen-Duc
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  2. Trung Le
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  3. Roland Bammer
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  4. He Zhao
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  5. Jianfei Cai
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  6. Dinh Phung
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Corresponding author

Correspondence to Thanh Nguyen-Duc .

Editor information

Editors and Affiliations

  1. Icahn School of Medicine, Mount Sinai, NYC, NY, USA, Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  2. Emory University, Atlanta, GA, USA

    Anant Madabhushi

  3. Queen's University, Kingston, ON, Canada

    Parvin Mousavi

  4. The University of British Columbia, Vancouver, BC, Canada

    Septimiu Salcudean

  5. Yale University, New Haven, CT, USA

    James Duncan

  6. IBM Research, San Jose, CA, USA

    Tanveer Syeda-Mahmood

  7. Johns Hopkins University, Baltimore, MD, USA

    Russell Taylor

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Nguyen-Duc, T., Le, T., Bammer, R., Zhao, H., Cai, J., Phung, D. (2023). Cross-Adversarial Local Distribution Regularization for Semi-supervised Medical Image Segmentation. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14220. Springer, Cham. https://doi.org/10.1007/978-3-031-43907-0_18

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  • DOI: https://doi.org/10.1007/978-3-031-43907-0_18

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