ArticleGeroScience2024
Decoding the best automated segmentation tools for vascular white matter hyperintensities in the aging brain: a clinician's guide to precision and purpose.
Article in GeroScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Cognitive dispersion is associated with white matter hyperintensities in amyloid positive older adults.The Clinical neuropsychologist · 2026Article
- Comparison of automated white matter lesion segmentation approaches for use in large, multi-site data analyses in Parkinson's disease.Frontiers in neuroscience · 2026Article
- Periventricular gradients in axonal and myelin microstructure are present in people with multiple sclerosis having low and high disability.Multiple sclerosis (Houndmills, Basingstoke, England) · 2026Article
- Effect of vascular lesion preprocessing on Brain Intensity AbNormality Classification Algorithm (BIANCA) white matter hyperintensity segmentation.NeuroImage. Clinical · 2026Article
- Cancer and Aging Biomarkers: Classification, Early Detection Technologies and Emerging Research Trends.Biosensors · 2025Review
- Complementary MR measures of white matter and their relation to cardiovascular health and cognition.Scientific reports · 2025Article
- Accurate and robust segmentation of cerebral distal small arteries by DVNet with dual contextual path and vascular attention enhancement.Quantitative imaging in medicine and surgery · 2025Article
- Automatic segmentation of white matter lesions on multi-parametric MRI: convolutional neural network versus vision transformer.BMC neurology · 2025Article
- Cerebral small vessel disease lesion segmentation methods: A systematic review.Cerebral circulation - cognition and behavior · 2025Review
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Authors and funding
9 authors.
Funding
Abstract
White matter hyperintensities of vascular origin (WMH) are commonly found in individuals over 60 and increase in prevalence with age. The significance of WMH is well-documented, with strong associations with cognitive impairment, risk of stroke, mental health, and brain structure deterioration. Consequently, careful monitoring is crucial for the early identification and management of individuals at risk. Luckily, WMH are detectable and quantifiable on standard MRI through visual assessment scales, but it is time-consuming and has high rater variability. Addressing this issue, the main aim of our study is to decipher the utility of quantitative measures of WMH, assessed with automatic tools, in establishing risk profiles for cerebrovascular deterioration. For this purpose, first, we work to determine the most precise WMH segmentation open access tool compared to clinician manual segmentations (LST-LPA, LST-LGA, SAMSEG, and BIANCA), offering insights into methodology and usability to balance clinical precision with practical application. The results indicated that supervised algorithms (LST-LPA and BIANCA) were superior, particularly in detecting small WMH, and can improve their consistency when used in parallel with unsupervised tools (LST-LGA and SAMSEG). Additionally, to investigate the behavior and real clinical utility of these tools, we tested them in a real-world scenario (N = 300; age > 50 y.o. and MMSE > 26), proposing an imaging biomarker for moderate vascular damage. The results confirmed its capacity to effectively identify individuals at risk comparing the cognitive and brain structural profiles of cognitively healthy adults above and below the resulted threshold.
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