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Automated three-dimensional major white matter bundle segmentation using diffusion magnetic resonance imaging.

Christina Andica1,2, Koji Kamagata3, Shigeki Aoki4,3

  • 1Faculty of Health Data Science, Juntendo University, 6-8-1 Hinode, Urayasu, Chiba, 279-0013, Japan. christina@juntendo.ac.jp.

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Summary
This summary is machine-generated.

Automated white matter segmentation using diffusion MRI fiber tractography offers reproducible analysis of brain anatomy and diseases. This review covers validated methods like TRACULA, AFQ, and TractSeg for efficient streamline extraction.

Keywords:
AutomaticDiffusion magnetic resonance imagingTractographyWhite matter

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Human Brain Anatomy

Background:

  • Diffusion magnetic resonance imaging (dMRI) fiber tractography is vital for evaluating white matter tracts in 3D, aiding research in brain anatomy, function, development, and diseases.
  • Manual streamline extraction for white matter bundle segmentation, while the gold standard, is labor-intensive, time-consuming, and lacks reproducibility.
  • Automated methods are being developed to overcome the limitations of manual segmentation in processing whole-brain tractograms.

Purpose of the Study:

  • To review and discuss well-validated automated approaches for white matter bundle segmentation.
  • To highlight end-to-end pipelines that enhance efficiency and reproducibility in tractography analysis.
  • To provide an overview of leading automated segmentation tools for researchers.

Main Methods:

  • Review of existing literature on automated white matter segmentation techniques.
  • Focus on end-to-end pipelines that streamline the segmentation process.
  • Discussion of specific validated methods: TRActs Constrained by UnderLying Anatomy (TRACULA), Automated Fiber Quantification (AFQ), and TractSeg.

Main Results:

  • Several automated approaches effectively reconstruct white matter tracts, addressing limitations of manual methods.
  • TRACULA, AFQ, and TractSeg represent well-validated strategies for automating white matter bundle segmentation.
  • These automated pipelines offer improved time efficiency and reproducibility compared to manual segmentation.

Conclusions:

  • Automated white matter bundle segmentation is crucial for advancing the study of brain anatomy, function, and disease.
  • The discussed methods (TRACULA, AFQ, TractSeg) provide robust and reproducible alternatives to manual streamline extraction.
  • Adoption of these automated pipelines can accelerate research in neuroscience and clinical applications.