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Related Experiment Video

Updated: Jul 19, 2025

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Optimizing automated white matter hyperintensity segmentation in individuals with stroke.

Jennifer K Ferris1,2, Bethany P Lo3, Mohamed Salah Khlif4

  • 1Graduate Program in Rehabilitation Sciences, University of British Columbia, Vancouver, BC, Canada.

Frontiers in Neuroimaging
|August 9, 2023
PubMed
Summary
This summary is machine-generated.

Automated white matter hyperintensity (WMH) segmentation tools require validation in stroke patients. Optimized BIANCA software performed well within datasets but failed to generalize, while SAMSEG showed better multi-site robustness.

Keywords:
BIANCAFSLSAMSEGlesion segmentationstrokewhite matter hyperintensity (WMH)

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Area of Science:

  • Neuroimaging
  • Stroke Research
  • Medical Image Analysis

Background:

  • White matter hyperintensities (WMHs) are stroke risk factors and common in stroke patients.
  • Accurate WMH quantification is crucial for understanding stroke recovery.
  • Automated segmentation methods offer efficiency but need validation in stroke populations.

Purpose of the Study:

  • To methodologically validate automated white matter hyperintensity (WMH) segmentation tools for stroke patients.
  • To compare the performance of BIANCA and SAMSEG in segmenting WMHs in individuals with stroke.

Main Methods:

  • Optimized parameters for FSL's BIANCA software on two independent, multi-site datasets.
  • Evaluated BIANCA's performance on within-dataset and cross-dataset generalization.
  • Contrasted BIANCA with SAMSEG, an unsupervised segmentation tool from FreeSurfer.

Main Results:

  • Optimized BIANCA showed good performance when trained and tested on the same dataset or mixed data.
  • BIANCA failed to generalize when a model trained on one dataset was applied to another.
  • SAMSEG demonstrated robustness with multi-site data, though with slightly lower accuracy than BIANCA on single-site data.

Conclusions:

  • Automated WMH segmentation requires careful validation in stroke populations.
  • BIANCA's generalizability is limited, necessitating site-specific optimization or alternative tools.
  • SAMSEG offers a more robust option for multi-site stroke studies, guiding future WMH analysis pipeline development.