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Automated White Matter Hyperintensity Segmentation Using Bayesian Model Selection: Assessment and Correlations with

Cassidy M Fiford1, Carole H Sudre2,3,4, Hugh Pemberton2

  • 1Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK. cassidy.fiford.10@ucl.ac.uk.

Neuroinformatics
|February 17, 2020
PubMed
Summary

Bayesian Model Selection (BaMoS) accurately segments white matter hyperintensities (WMH). This automated method predicts cognitive decline in various neurological conditions, proving valuable for large-scale Alzheimer's disease research.

Keywords:
Alzheimer’s diseaseAutomated segmentationMagnetic resonance imagingNeurodegenerationVascular pathologyWhite matter hyperintensities

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biomedical Image Analysis

Background:

  • White matter hyperintensities (WMH) are crucial biomarkers in neurological diseases.
  • Accurate, automated WMH segmentation is essential for large-scale studies.
  • Existing methods require manual input or lack robustness.

Purpose of the Study:

  • To evaluate Bayesian Model Selection (BaMoS) for automated WMH segmentation.
  • To compare BaMoS performance against semi-automated methods.
  • To assess BaMoS's ability to predict longitudinal cognitive change across different cognitive states.

Main Methods:

  • BaMoS, a hierarchical unsupervised framework, was used for WMH segmentation.
  • Magnetic resonance images from Alzheimer's Disease Neuroimaging Initiative (ADNI) were utilized.
  • Segmentation accuracy was validated against expert raters using Dice scores and correlation coefficients.
  • Predictive power for cognitive change was assessed using linear mixed-effect models in control and patient groups.

Main Results:

  • BaMoS demonstrated high agreement with expert segmentations (Dice score: 0.74, correlation: 0.96).
  • Segmentations were robust across varying WMH loads and MRI scanners.
  • BaMoS-derived WMH volumes significantly predicted cognitive decline in control, early Mild Cognitive Impairment (EMCI), and Subjective/Significant Memory Concern (SMC) groups.

Conclusions:

  • BaMoS offers a robust and accurate automated solution for WMH segmentation.
  • The framework is suitable for large-scale neuroimaging studies.
  • BaMoS-derived WMH metrics can serve as predictive biomarkers for cognitive decline in neurodegenerative diseases.