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Abstract #3482

Self-Supervised Transfer Learning for Infant Cerebellum Segmentation with Multi-Domain MRIs

Yue Sun1, Kun Gao1, Shihui Ying1, Weili Lin1, Gang Li1, Sijie Niu1, Mingxia Liu1, and Li Wang1
1Department of Radiology and Biomedical Research Imaging Center, UNC at Chapel Hill, Chapel Hill, NC, United States

This study develops a self-supervised transfer learning (SSTL) framework to generate reliable cerebellum segmentations for infant subjects with multi-domain MRIs, aiming to alleviate the domain shift between different time-points/sites and improve the generalization ability. Experiments demonstrate that by transferring limited manual labels from late time-points (or a specific site) with high tissue contrast to early time-points (or other sites) with low contrast, our method achieves improved performance and can be applied to other tasks, especially for those with multi-site data.

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