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Tractostorm 2: Optimizing tractography dissection reproducibility with segmentation protocol dissemination.

Francois Rheault1, Kurt G Schilling2,3, Alex Valcourt-Caron4

  • 1Electrical and Computer Engineering, Vanderbilt University, Nashville, Tennessee, USA.

Human Brain Mapping
|February 10, 2022
PubMed
Summary
This summary is machine-generated.

An online tractography course and dissection protocol significantly improved the reproducibility of white-matter pathway segmentation in neuroimaging studies. This educational approach enhances consistency among raters over time.

Keywords:
WM pathwaysdiffusion MRIreproducibilitysegmentationtractographyvirtual dissection

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Anatomy

Background:

  • Accurate segmentation of brain structures is vital for neuroimaging studies.
  • Variability in interpreting and executing dissections of white-matter (WM) pathways hinders consensus.
  • Existing methods for defining and teaching WM pathway anatomy are challenging.

Purpose of the Study:

  • To evaluate an online educational tractography course.
  • To investigate the impact of learning and practicing a dissection protocol on interrater reproducibility.
  • To quantify the effect of virtual education on WM pathway segmentation consistency.

Main Methods:

  • Twenty raters performed dissections of three WM bundles across five subjects with four timepoints.
  • Reproducibility was quantified using voxel-based Dice scores.
  • The study assessed the impact of an online educational module and protocol practice.

Main Results:

  • The combined dissection protocol and online course achieved high reproducibility (Dice score 0.85-0.90).
  • Reproducibility remained stable over time, indicating consistent performance after initial learning.
  • Virtual educational sessions demonstrably increased reproducibility compared to previous methods.

Conclusions:

  • The developed protocol and online course enhance the reproducibility of white-matter pathway dissections.
  • Virtual learning environments can effectively improve consistency in neuroanatomical segmentation.
  • Findings can inform the design of future dissection protocols and neuroanatomical definitions.