ArticleThe Journal of international medical research2020
Validation and comparison of two automated methods for quantifying brain white matter hyperintensities of presumed vascular origin.
Article in The Journal of international medical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- White Matter Hyperintensities and Neuropsychiatric Symptoms in Neurodegenerative Diseases.Journal of clinical neurology (Seoul, Korea) · 2026Article
- Hypertension is the Main Vascular Risk Factor for Cognitive Impairment, Microvascular Pathology and Brain Atrophy in Alzheimer's Disease.Current Alzheimer research · 2026Article
- Field-cycling imaging yields repeatable brain RMagma (New York, N.Y.) · 2025Article
- Impact of Brain Lesion Characteristics on Motor Function and Cortical Reorganization in Hemiplegic Cerebral Palsy.Medicina (Kaunas, Lithuania) · 2025Article
- Augmenting radiological assessment of imaging evident dementias with radiomic analysis.NPJ dementia · 2025Article
- Current and Future Developments in Imaging and Treatment of White Matter Disease: A Systematic Review.Cureus · 2023Review
- Cocaine use is associated with cerebral white matter hyperintensities in HIV disease.Annals of clinical and translational neurology · 2023Article
- HDL-C/LDL-C and Risk of Cerebral White Matter Hyperintensities: A Cross-Sectional Study.International journal of general medicine · 2023Article
- Influence of regional white matter hyperintensity volume and apolipoprotein E ε4 status on hippocampal volume in healthy older adults.Hippocampus · 2021Article
- Widespread Disruptions of White Matter in Familial Multiple Sclerosis: DTI and NODDI Study.Frontiers in neurology · 2021Article
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5 authors.
Funding
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Abstract
objectivesWhite matter hyperintensities (WMH) are a common imaging finding indicative of cerebral small vessel disease. Lesion segmentation algorithms have been developed to overcome issues arising from visual rating scales. In this study, we evaluated two automated methods and compared them to visual and manual segmentation to determine the most robust algorithm provided by the open-source Lesion Segmentation Toolbox (LST).
methodsWe compared WMH data from visual ratings (Scheltens' scale) with those derived from algorithms provided within LST. We then compared spatial and volumetric WMH data derived from manually-delineated lesion maps with WMH data and lesion maps provided by the LST algorithms.
resultsWe identified optimal initial thresholds for algorithms provided by LST compared with visual ratings (Lesion Growth Algorithm (LGA): initial κ and lesion probability thresholds, 0.5; Lesion Probability Algorithm (LPA) lesion probability threshold, 0.65). LGA was found to perform better then LPA compared with manual segmentation.
conclusionLGA appeared to be the most suitable algorithm for quantifying WMH in relation to cerebral small vessel disease, compared with Scheltens' score and manual segmentation. LGA offers a user-friendly, effective WMH segmentation method in the research environment.
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