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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