Hyperplane-based tract parcellations for improved anatomical coherence in tractometry

Motivation: The subdivision of WM tracts into individual parcels required for tractometry analysis, suffers from blurred parcel borders and wrong assignments of complete tract regions, particularly in fanning tracts. Goal(s): The goal of this work is to overcome these limitations with a new parcellation approach that yields clearly delineated tract parcels and avoids wrong parcel assignments even in challenging tracts. Approach: We propose a self-supervised approach based on optimally separating hyperplanes, obtained using large-margin classifiers, to separate tracts into parcels. Results: We compare our approach to two popular state-of-the-art approaches and clearly show markedly improved results in 24 tracts and 98 subjects. Impact: A new method to parcellate tracts for fiber tractometry, avoiding frequent errors of state-of-the-art approaches, particularly in complex tracts with a fanning topology. This might lead to improved tractometry analysis and potentially insights that were not possible with previous approaches.