ArticleNeuroradiology2022
A deep learning algorithm for white matter hyperintensity lesion detection and segmentation.
Article in Neuroradiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 26 citations in OpenAlex.
- Development and validation of a deep learning-based automatic segmentation and classification of cerebral white matter hyperintensities.European radiology · 2026Article
- Deep optimization-guided hybrid neural network for accurate detection and segmentation of white matter hyperintensities in clinical MRI images.Scientific reports · 2026Article
- Advantage of grading classification using volumetric artificial intelligence for periventricular hyperintensity and deep subcortical white matter hyperintensity.Scientific reports · 2025Article
- Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review.Royal Society open science · 2025Review
- A comparative study of risk factors in predictive models for cognitive dysfunction in patients with leukoaraiosis based on machine learning algorithms.Frontiers in neuroscience · 2025Article
- Deep learning-based automated segmentation for the quantitative diagnosis of cerebral small vessel disease via multisequence MRI.Frontiers in neurology · 2025Article
- Cerebral small vessel disease lesion segmentation methods: A systematic review.Cerebral circulation - cognition and behavior · 2025Review
- Application of artificial intelligence-based magnetic resonance imaging in diagnosis of cerebral small vessel disease.CNS neuroscience & therapeutics · 2024Review
- 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
- Multi-stage semi-supervised learning enhances white matter hyperintensity segmentation.Frontiers in computational neuroscience · 2024Article
- Toward Automated Detection of Silent Cerebral Infarcts in Children and Young Adults With Sickle Cell Anemia.Stroke · 2023Article
- Characterization of MRI White Matter Signal Abnormalities in the Pediatric Population.Children (Basel, Switzerland) · 2023Article
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Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
Abstract
purposeWhite matter hyperintensity (WMHI) lesions on MR images are an important indication of various types of brain diseases that involve inflammation and blood vessel abnormalities. Automated quantification of the WMHI can be valuable for the clinical management of patients, but existing automated software is often developed for a single type of disease and may not be applicable for clinical scans with thick slices and different scanning protocols. The purpose of the study is to develop and validate an algorithm for automatic quantification of white matter hyperintensity suitable for heterogeneous MRI data with different disease types.
methodsWe developed and evaluated "DeepWML", a deep learning method for fully automated white matter lesion (WML) segmentation of multicentre FLAIR images. We used MRI from 507 patients, including three distinct white matter diseases, obtained in 9 centres, with a wide range of scanners and acquisition protocols. The automated delineation tool was evaluated through quantitative parameters of Dice similarity, sensitivity and precision compared to manual delineation (gold standard).
resultsThe overall median Dice similarity coefficient was 0.78 (range 0.64 ~ 0.86) across the three disease types and multiple centres. The median sensitivity and precision were 0.84 (range 0.67 ~ 0.94) and 0.81 (range 0.64 ~ 0.92), respectively. The tool's performance increased with larger lesion volumes.
conclusionDeepWML was successfully applied to a wide spectrum of MRI data in the three white matter disease types, which has the potential to improve the practical workflow of white matter lesion delineation.
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