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Self-supervised learning with application for infant cerebellum segmentation and analysis

Author

Listed:
  • Yue Sun

    (University of North Carolina at Chapel Hill)

  • Limei Wang

    (University of North Carolina at Chapel Hill)

  • Kun Gao

    (University of North Carolina at Chapel Hill)

  • Shihui Ying

    (University of North Carolina at Chapel Hill)

  • Weili Lin

    (University of North Carolina at Chapel Hill)

  • Kathryn L. Humphreys

    (Vanderbilt University
    School of Medicine, Tulane University)

  • Gang Li

    (University of North Carolina at Chapel Hill)

  • Sijie Niu

    (University of North Carolina at Chapel Hill)

  • Mingxia Liu

    (University of North Carolina at Chapel Hill)

  • Li Wang

    (University of North Carolina at Chapel Hill)

Abstract

Accurate tissue segmentation is critical to characterize early cerebellar development in the first two postnatal years. However, challenges in tissue segmentation arising from tightly-folded cortex, low and dynamic tissue contrast, and large inter-site data heterogeneity have limited our understanding of early cerebellar development. In this paper, we propose an accurate self-supervised learning framework for infant cerebellum segmentation. We validate its accuracy using 358 subjects from three datasets. Our results suggest the first six months exhibit the most rapid and dynamic changes, with gray matter (GM) playing a dominant role in cerebellar growth over white matter (WM). We also find both GM and WM volumes are larger in males than females, and GM and WM volumes are larger in autistic males than neurotypical males. Application of our method to a larger population will fuel more cerebellar studies, ultimately advancing our comprehension of its structure and function in neurotypical and disordered development.

Suggested Citation

  • Yue Sun & Limei Wang & Kun Gao & Shihui Ying & Weili Lin & Kathryn L. Humphreys & Gang Li & Sijie Niu & Mingxia Liu & Li Wang, 2023. "Self-supervised learning with application for infant cerebellum segmentation and analysis," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40446-z
    DOI: 10.1038/s41467-023-40446-z
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    References listed on IDEAS

    as
    1. Nalin Payakachat & J. Mick Tilford & Wendy J. Ungar, 2016. "National Database for Autism Research (NDAR): Big Data Opportunities for Health Services Research and Health Technology Assessment," PharmacoEconomics, Springer, vol. 34(2), pages 127-138, February.
    2. Nalin Payakachat & J. Tilford & Wendy Ungar, 2016. "National Database for Autism Research (NDAR): Big Data Opportunities for Health Services Research and Health Technology Assessment," PharmacoEconomics, Springer, vol. 34(2), pages 127-138, February.
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