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

TL;DR

This paper introduces a novel unsupervised deep learning framework for white matter fiber clustering that is robust to data ambiguities and incorporates anatomical information, leading to improved accuracy and efficiency.

Contribution

It presents a self-supervised deep learning method for fiber clustering that handles data ambiguity and integrates anatomical segmentation for better results.

Findings

Superior clustering performance on Human Connectome Project data

Effective outlier removal through cluster assignment probabilities

Enhanced anatomical coherence in fiber parcellation

Abstract

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.