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Curriculum Semi-supervised Segmentation

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 (MICCAI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11765))

Abstract

This study investigates a curriculum-style strategy for semi-supervised CNN segmentation, which devises a regression network to learn image-level information such as the size of the target region. These regressions are used to effectively regularize the segmentation network, constraining the softmax predictions of the unlabeled images to match the inferred label distributions. Our framework is based on inequality constraints, which tolerate uncertainties in the inferred knowledge, e.g., regressed region size. It can be used for a large variety of region attributes. We evaluated our approach for left ventricle segmentation in magnetic resonance images (MRI), and compared it to standard proposal-based semi-supervision strategies. Our method achieves competitive results, leveraging unlabeled data in a more efficient manner and approaching full-supervision performance.

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Notes

  1. 1.

    https://github.com/LIVIAETS/semi_curriculum.

  2. 2.

    Note that the proposal-based methods in [1] use CRF to boost performance.

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Authors and Affiliations

  1. ÉTS Montréal, Montréal, Canada

    Hoel Kervadec, Jose Dolz, Éric Granger & Ismail Ben Ayed

Authors
  1. Hoel Kervadec
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  2. Jose Dolz
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  3. Éric Granger
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  4. Ismail Ben Ayed
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Corresponding author

Correspondence to Hoel Kervadec .

Editor information

Editors and Affiliations

  1. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Dinggang Shen

  2. University of Georgia, Athens, GA, USA

    Tianming Liu

  3. Western University, London, ON, Canada

    Terry M. Peters

  4. Yale University, New Haven, CT, USA

    Lawrence H. Staib

  5. University of Strasbourg, Illkirch, France

    Caroline Essert

  6. United Imaging Intelligence, Shanghai, China

    Sean Zhou

  7. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Pew-Thian Yap

  8. Western University, London, ON, Canada

    Ali Khan

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Kervadec, H., Dolz, J., Granger, É., Ben Ayed, I. (2019). Curriculum Semi-supervised Segmentation. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11765. Springer, Cham. https://doi.org/10.1007/978-3-030-32245-8_63

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  • DOI: https://doi.org/10.1007/978-3-030-32245-8_63

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32244-1

  • Online ISBN: 978-3-030-32245-8

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