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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