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Abstract #3303

Anatomically Informed Unsupervised Deep Learning for Fast and Effective White Matter Fiber Clustering

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


We propose a novel unsupervised deep learning framework for white matter fiber clustering. Self-supervised learning is adopted to enable joint deep embedding and cluster assignment. Anatomical information is incorporated into the neural network to improve anatomical coherence. In addition, outlier removal is performed to further improve cluster quality. Our method is evaluated on three datasets and showed superior performance in terms of cluster compactness, anatomical coherence and generalization across subjects compared to several state-of-the-art algorithms.

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