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

Yue Sun, Limei Wang, Kun Gao, Shihui Ying, Weili Lin, Kathryn L. Humphreys, Gang Li, Sijie Niu, Mingxia Liu () and Li Wang ()
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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
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

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-40446-z

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