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Deep Fiber Clustering: Anatomically Informed Unsupervised Deep Learning for Fast and Effective White Matter Parcellation

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 (MICCAI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12907))

Abstract

White matter fiber clustering (WMFC) enables parcellation of white matter tractography for applications such as disease classification and anatomical tract segmentation. However, the lack of ground truth and the ambiguity of fiber data (the points along a fiber can equivalently be represented in forward or reverse order) pose challenges to this task. We propose a novel WMFC framework based on unsupervised deep learning. We solve the unsupervised clustering problem as a self-supervised learning task. Specifically, we use a convolutional neural network to learn embeddings of input fibers, using pairwise fiber distances as pseudo annotations. This enables WMFC that is insensitive to fiber point ordering. In addition, anatomical coherence of fiber clusters is improved by incorporating brain anatomical segmentation data. The proposed framework enables outlier removal in a natural way by rejecting fibers with low cluster assignment probability. We train and evaluate our method using 200 datasets from the Human Connectome Project. Results demonstrate superior performance and efficiency of the proposed approach.

We acknowledge funding provided by the following National Institutes of Health (NIH) grants: R01MH125860, R01MH119222, R01MH074794, and P41EB015902.

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Authors and Affiliations

  1. Harvard Medical School, Boston, MA, USA

    Yuqian Chen, Nikos Makris, Yogesh Rathi, Fan Zhang & Lauren J. O’Donnell

  2. The University of Sydney, Sydney, NSW, Australia

    Yuqian Chen, Chaoyi Zhang & Weidong Cai

  3. The University of New South Wales, Sydney, NSW, Australia

    Yang Song

Authors
  1. Yuqian Chen
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  2. Chaoyi Zhang
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  3. Yang Song
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  4. Nikos Makris
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  5. Yogesh Rathi
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  6. Weidong Cai
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  7. Fan Zhang
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  8. Lauren J. O’Donnell
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Corresponding author

Correspondence to Fan Zhang .

Editor information

Editors and Affiliations

  1. Erasmus MC - University Medical Center Rotterdam, Rotterdam, The Netherlands

    Marleen de Bruijne

  2. University of Basel, Allschwil, Switzerland

    Philippe C. Cattin

  3. Inria Nancy Grand Est, Villers-lès-Nancy, France

    Stéphane Cotin

  4. ICube, Université de Strasbourg, CNRS, Strasbourg,, France

    Nicolas Padoy

  5. National Center for Tumor Diseases (NCT/UCC), Dresden, Germany

    Stefanie Speidel

  6. Tencent Jarvis Lab, Shenzhen, China

    Yefeng Zheng

  7. ICube, Université de Strasbourg, CNRS, Strasbourg, France

    Caroline Essert

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Chen, Y. et al. (2021). Deep Fiber Clustering: Anatomically Informed Unsupervised Deep Learning for Fast and Effective White Matter Parcellation. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12907. Springer, Cham. https://doi.org/10.1007/978-3-030-87234-2_47

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  • DOI: https://doi.org/10.1007/978-3-030-87234-2_47

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  • Online ISBN: 978-3-030-87234-2

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