Evolution of white matter hyperintensity segmentation methods and implementation over the past two decades; an
Maryam Rahmani1,2, Donna Dierker1,2, Lauren Yaeger
1Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
Brain Imaging and Behavior
|July 31, 2024
Summary
This review analyzes white matter hyperintensity (WMH) segmentation methods over 20 years. Deep learning is increasingly used, yet visual scales persist, highlighting the need for standardization in WMH research.
Area of Science:
- Neuroimaging
- Medical Image Analysis
- Systematic Review
Background:
- White matter hyperintensities (WMH) are crucial biomarkers in neurological and psychiatric conditions.
- Understanding WMH prevalence and segmentation techniques is vital for disease research.
- Existing literature shows diverse approaches to WMH analysis.
Purpose of the Study:
- To systematically review WMH segmentation literature from 2000-2022.
- To categorize WMH tools by methodology, prevalence, and implementation.
- To identify trends and discrepancies in WMH segmentation techniques.
Main Methods:
- Systematic literature search adhering to PRISMA guidelines.
- Categorization of WMH segmentation tools based on methodology.
- Inclusion of articles with openly available techniques and WMH as primary outcome.
Main Results:
- Analysis of 1007 visual rating scales, 118 pipeline, and 509 implementation articles.
- Predominant cohorts: aging, dementia, psychiatric disorders, small vessel disease.
- Deep learning is the most developed segmentation technique; SPM is the most utilized quantitative method.
Conclusions:
- Deep learning shows increased development in WMH segmentation.
- Visual rating scales remain prevalent despite advanced quantitative methods.
- Standardization of WMH segmentation techniques is recommended for future research.
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