  White matter fiber clustering (WMFC) enables parcellation of white matter
tractography for applications such as disease classification and anatomical
tract segmentation. However, the lack of ground truth and the ambiguity of
fiber data (the points along a fiber can equivalently be represented in forward
or reverse order) pose challenges to this task. We propose a novel WMFC
framework based on unsupervised deep learning. We solve the unsupervised
clustering problem as a self-supervised learning task. Specifically, we use a
convolutional neural network to learn embeddings of input fibers, using
pairwise fiber distances as pseudo annotations. This enables WMFC that is
insensitive to fiber point ordering. In addition, anatomical coherence of fiber
clusters is improved by incorporating brain anatomical segmentation data. The
proposed framework enables outlier removal in a natural way by rejecting fibers
with low cluster assignment probability. We train and evaluate our method using
200 datasets from the Human Connectome Project. Results demonstrate superior
performance and efficiency of the proposed approach.
