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Semi-supervised Breast Lesion Segmentation Using Local Cross Triplet Loss for Ultrafast Dynamic Contrast-Enhanced MRI

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Computer Vision – ACCV 2022 (ACCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13846))

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Abstract

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and its fast variant, ultrafast DCE-MRI, are useful for the management of breast cancer. Segmentation of breast lesions is necessary for automatic clinical decision support. Despite the advantage of acquisition time, existing segmentation studies on ultrafast DCE-MRI are scarce, and they are mostly fully supervised studies with high annotation costs. Herein, we propose a semi-supervised segmentation approach that can be trained with small amounts of annotations for ultrafast DCE-MRI. A time difference map is proposed to incorporate the distinct time-varying enhancement pattern of the lesion. Furthermore, we present a novel loss function that efficiently distinguishes breast lesions from non-lesions based on triple loss. This loss reduces the potential false positives induced by the time difference map. Our approach is compared to that of five competing methods using the dice similarity coefficient and two boundary-based metrics. Compared to other models, our approach achieves better segmentation results using small amounts of annotations, especially for boundary-based metrics relevant to spatially continuous breast lesions. An ablation study demonstrates the incremental effects of our study. Our code is available on GitHub (https://github.com/yt-oh96/SSL-CTL).

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Acknowledgments

This research was supported by the National Research Foundation (NRF-2020M3E5D2A01084892), Institute for Basic Science (IBSR015- D1), Ministry of Science and ICT (IITP-2020-2018-0-01798), AI Graduate School Support Program (2019-0-00421), ICT Creative Consilience program (IITP-2020-0-01821), and Artificial Intelligence Innovation Hub (2021-0-02068).

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Authors and Affiliations

  1. Sungkyunkwan University, Suwon, Republic of Korea

    Young-tack Oh & Hyunjin Park

  2. Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

    Eunsook Ko

  3. Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea

    Hyunjin Park

Authors
  1. Young-tack Oh
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  2. Eunsook Ko
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  3. Hyunjin Park
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Corresponding authors

Correspondence to Eunsook Ko or Hyunjin Park .

Editor information

Editors and Affiliations

  1. University of Wollongong, Wollongong, NSW, Australia

    Lei Wang

  2. University of Bonn, Bonn, Germany

    Juergen Gall

  3. University of Adelaide, Adelaide, SA, Australia

    Tat-Jun Chin

  4. National Institute of Informatics, Tokyo, Japan

    Imari Sato

  5. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

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Oh, Yt., Ko, E., Park, H. (2023). Semi-supervised Breast Lesion Segmentation Using Local Cross Triplet Loss for Ultrafast Dynamic Contrast-Enhanced MRI. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13846. Springer, Cham. https://doi.org/10.1007/978-3-031-26351-4_13

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  • DOI: https://doi.org/10.1007/978-3-031-26351-4_13

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