Tractometry, also known as tract profiling, is a powerful technique for probing microstructural properties along white matter (WM) tracts. A prerequisite for tractography-based tractometry is bundle parcellation-the subdivision of WM bundles into smaller segments where microstructural measures can be computed. However, existing parcellation methods lack consistency across bundles and timepoints, which reduces reproducibility and limits their utility for both longitudinal and cross-sectional studies. Moreover, these methods typically depend on tractography and bundle segmentation, two processes that are computationally expensive and often highly variable. In this work, we introduce BundleParc, a consistent and tractography-free bundle parcellation method. Instead of relying on streamline generation, BundleParc maps fiber orientation distribution function (fODF) volumes directly to label maps. Rigorous evaluation on research and clinical cohorts show that BundleParc is not only much simpler than state-of-the-art tract-based profiling methods, it is also consistently more accurate, robust and reproducible. With these results, BundleParc is a new solution for fast, easy-to-use, and off-the-shelf bundle segmentation and parcellation.
Keywords: Deep learning; Diffusion MRI; Tractography; Tractometry.
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