We propose a deep-learning-based framework, Superficial White Matter Analysis (SupWMA), which performs an efficient and consistent parcellation of 198 SWM clusters from whole-brain tractography. A point-cloud-based network is developed for our SWM parcellation task, and supervised contrastive learning enables more discriminative representations between plausible streamlines and outliers. SupWMA obtains highly consistent and accurate SWM parcellation results on a large tractography dataset with ground truth labels and on three independently acquired testing datasets from individuals across ages and health conditions.
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