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Abstract #3302

An efficient deep learning framework for consistent superficial white matter tractography parcellation

Tengfei Xue1,2, Fan Zhang1, Chaoyi Zhang2, Yuqian Chen1,2, Yang Song3, Nikos Makris1, Yogesh Rathi1, Weidong Cai2, and Lauren Jean O’Donnell1
1Harvard Medical School, Boston, MA, United States, 2The University of Sydney, Sydney, Australia, 3University of New South Wales, Sydney, Australia


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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