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White matter parcellation classifies tractography streamlines into clustersor anatomically meaningful tracts to enable quantification and visualization.Most parcellation methods focus on the deep white matter (DWM), while fewermethods address the superficial white matter (SWM) due to its complexity. Wepropose a deep-learning-based framework, Superficial White Matter Analysis(SupWMA), that performs an efficient and consistent parcellation of 198 SWMclusters from whole-brain tractography. A point-cloud-based network is modifiedfor our SWM parcellation task, and supervised contrastive learning enables morediscriminative representations between plausible streamlines and outliers. Weperform evaluation on a large tractography dataset with ground truth labels andon three independently acquired testing datasets from individuals across agesand health conditions. Compared to several state-of-the-art methods, SupWMAobtains a highly consistent and accurate SWM parcellation result. In addition,the computational speed of SupWMA is much faster than other methods.

SupWMA: Consistent and Efficient Tractography Parcellation of Superficial White Matter with Deep Learning