SupWMA: Consistent and Efficient Tractography Parcellation of Superficial White Matter with Deep Learning
This work addresses the challenge of efficient and consistent SWM parcellation for researchers in neuroimaging, enabling better quantification and visualization, though it is incremental as it adapts existing deep learning techniques to a specific domain.
The authors tackled the problem of parcellating superficial white matter (SWM) from whole-brain tractography, which is complex and less addressed than deep white matter, by proposing SupWMA, a deep-learning framework that achieves highly consistent and accurate results with much faster computational speed compared to state-of-the-art methods.
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.