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