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BundleParc: Consistent white matter bundle parcellation without tractography.

Antoine Théberge1, Zineb El Yamani2, Muhamed Barakovic3

  • 1Videos & Images Theory and Analytics Laboratory (VITAL), Faculté des Sciences, Université de Sherbrooke, 2500 Bd. de l'Université, Sherbrooke, J1K2R1, QC, Canada; Sherbrooke Connectivity and Imaging Lab (SCIL), Faculté des Sciences, Université de Sherbrooke, 2500 Bd. de l'Université, Sherbrooke, J1K2R1, QC, Canada.

Medical Image Analysis
|April 26, 2026
PubMed
Summary
This summary is machine-generated.

BundleParc offers a new, simpler method for white matter (WM) bundle parcellation. This tractography-free approach improves accuracy and reproducibility in neuroimaging studies.

Keywords:
Deep learningDiffusion MRITractographyTractometry

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Area of Science:

  • Neuroimaging
  • White Matter Anatomy
  • Computational Neuroscience

Background:

  • Tractometry analyzes microstructural properties of white matter (WM) tracts.
  • Accurate white matter bundle parcellation is crucial for tractometry.
  • Current methods are inconsistent, computationally expensive, and lack reproducibility.

Purpose of the Study:

  • Introduce BundleParc, a novel, tractography-free bundle parcellation method.
  • Develop a consistent, accurate, and reproducible parcellation technique.
  • Simplify and enhance the utility of white matter bundle segmentation.

Main Methods:

  • BundleParc directly maps fiber orientation distribution function (fODF) volumes to label maps.
  • Avoids computationally intensive streamline generation and tractography.
  • Utilizes a direct mapping approach for parcellation.

Main Results:

  • BundleParc demonstrates superior accuracy, robustness, and reproducibility compared to state-of-the-art methods.
  • The method is significantly simpler and faster than existing tract-based profiling techniques.
  • Evaluations on research and clinical cohorts confirm its effectiveness.

Conclusions:

  • BundleParc provides a consistent and tractography-free solution for bundle parcellation.
  • It offers a significant advancement for reproducible and efficient white matter analysis.
  • The method is suitable for fast, easy-to-use, off-the-shelf bundle segmentation.