ArticleScientific reports2019
Performance of five automated white matter hyperintensity segmentation methods in a multicenter dataset.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed, 54 citations in OpenAlex.
- Magnetic Resonance Imaging in Cerebral Small Vessel Disease-Related Depression: From Visual Scoring to Artificial Intelligence.CNS neuroscience & therapeutics · 2026Review
- Lesion-Level Subtypes of White Matter Hyperintensity Evolution Beyond Spatial Location.Neurology · 2026Observational
- Incorporating normal periventricular changes for enhanced pathological white matter hyperintensity segmentation: on multiclass deep learning approaches.Biomedical engineering online · 2026Article
- Benchmark White Matter Hyperintensity Segmentation Methods Fail on Heterogeneous Clinical MRI: A New Dataset and Deep Learning-Based Solutions.Journal of imaging informatics in medicine · 2026Article
- Effect of vascular lesion preprocessing on Brain Intensity AbNormality Classification Algorithm (BIANCA) white matter hyperintensity segmentation.NeuroImage. Clinical · 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
- Artificial intelligence for white matter hyperintensity segmentation: performance, determinants, and clinical implications.Frontiers in aging neuroscience · 2026Article
- Burden-dependent associations of serum sTREM2 with white matter injury and cognitive heterogeneity in cerebral small vessel disease.Frontiers in aging neuroscience · 2026Article
- Advantage of grading classification using volumetric artificial intelligence for periventricular hyperintensity and deep subcortical white matter hyperintensity.Scientific reports · 2025Article
- WMH-DualTasker: A Weakly Supervised Deep Learning Model for Automated White Matter Hyperintensities Segmentation and Visual Rating Prediction.Human brain mapping · 2025Article
- A Case for Automated Segmentation of MRI Data in Neurodegenerative Diseases: Type II GM1 Gangliosidosis.NeuroSci · 2025Article
- Stroke etiology and white matter burden in women with and without migraine.The journal of headache and pain · 2025Article
- Cerebral small vessel disease lesion segmentation methods: A systematic review.Cerebral circulation - cognition and behavior · 2025Review
- Multi-center evaluation of Neurophet AQUA for brain MRI segmentation: T1 compared with FreeSurfer and T2-FLAIR compared with ground truth.Frontiers in neurology · 2025Article
- Decoding the best automated segmentation tools for vascular white matter hyperintensities in the aging brain: a clinician's guide to precision and purpose.GeroScience · 2024Article
- Artificial intelligence for volumetric measurement of cerebral white matter hyperintensities on thick-slice fluid-attenuated inversion recovery (FLAIR) magnetic resonance images from multiple centers.Scientific reports · 2024Article
- Early detection of white matter hyperintensities using SHIVA-WMH detector.Human brain mapping · 2024Article
- More Similar than Different: Memory, Executive Functions, Cortical Thickness, and Glucose Metabolism in Biomarker-Positive Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia.Journal of Alzheimer's disease reports · 2024Article
- BIANCA-MS: An optimized tool for automated multiple sclerosis lesion segmentation.Human brain mapping · 2023Article
- Toward Automated Detection of Silent Cerebral Infarcts in Children and Young Adults With Sickle Cell Anemia.Stroke · 2023Article
Corrections and comments
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Authors and funding
11 authors at 7 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
White matter hyperintensities (WMHs) are a common manifestation of cerebral small vessel disease, that is increasingly studied with large, pooled multicenter datasets. This data pooling increases statistical power, but poses challenges for automated WMH segmentation. Although there is extensive literature on the evaluation of automated WMH segmentation methods, such evaluations in a multicenter setting are lacking. We performed WMH segmentations in sixty patients scanned on six different magnetic resonance imaging (MRI) scanners (10 patients per scanner) using five freely available and fully-automated WMH segmentation methods (Cascade, kNN-TTP, Lesion-TOADS, LST-LGA and LST-LPA). Different MRI scanner vendors and field strengths were included. We compared these automated WMH segmentations with manual WMH segmentations as a reference. Performance of each method both within and across scanners was assessed using spatial and volumetric correspondence with the reference segmentations by Dice's similarity coefficient (DSC) and intra-class correlation coefficient (ICC) respectively. We found the best performance, both within and across scanners, for kNN-TTP, followed by LST-LPA and LST-LGA, with worse performance for Lesion-TOADS and Cascade. Our findings can serve as a guide for choosing a method and also highlight the importance to further improve and evaluate consistency of methods in a multicenter setting.
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