ArticleHuman brain mapping2022
White matter hyperintensities segmentation using an ensemble of neural networks.
Article in Human brain mapping, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 37 citations in OpenAlex.
- Evolution of white matter hyperintensity segmentation methods and implementation over the past two decades; an incomplete shift towards deep learning.Brain imaging and behavior · 2024Pooled it
- A 25-Year Retrospective of the Use of AI for Diagnosing Acute Stroke: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Development and validation of a deep learning-based automatic segmentation and classification of cerebral white matter hyperintensities.European radiology · 2026Article
- Artificial intelligence for white matter hyperintensity segmentation: performance, determinants, and clinical implications.Frontiers in aging neuroscience · 2026Article
- Quantifying White Matter Hyperintensities: Automated Volumetry Compared with Visual Grading Scales.Medicina (Kaunas, Lithuania) · 2025Article
- Deep Learning Analysis of White Matter Hyperintensity and its Association with Comprehensive Vascular Factors in Two Large General Populations.Journal of imaging informatics in medicine · 2025Article
- Obesity and brain volumes: mediation by cardiometabolic and inflammatory measures.Stroke and vascular neurology · 2025Article
- Advancements in Frank's sign Identification using deep learning on 3D brain MRI.Scientific reports · 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
- Automated Segmentation of MRI White Matter Hyperintensities in 8421 Patients with Acute Ischemic Stroke.AJNR. American journal of neuroradiology · 2024Article
- LST-AI: a Deep Learning Ensemble for Accurate MS Lesion Segmentation.medRxiv : the preprint server for health sciences · 2024Article
- Early detection of white matter hyperintensities using SHIVA-WMH detector.Human brain mapping · 2024Article
- Peripapillary vessels density is closely related to cerebral white matter hyperintensities: An OCTA study.PloS one · 2024Article
- Adherence to a healthy lifestyle and brain structural imaging markers.European journal of epidemiology · 2023Article
- Article
- Higher-resolution quantification of white matter hypointensities by large-scale transfer learning from 2D images on the JPSC-AD cohort.Human brain mapping · 2022Article
- White matter hyperintensities segmentation using an ensemble of neural networks.Human brain mapping · 2022Article
- Ensemble learningFrontiers in neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
White matter hyperintensities (WMHs) represent the most common neuroimaging marker of cerebral small vessel disease (CSVD). The volume and location of WMHs are important clinical measures. We present a pipeline using deep fully convolutional network and ensemble models, combining U-Net, SE-Net, and multi-scale features, to automatically segment WMHs and estimate their volumes and locations. We evaluated our method in two datasets: a clinical routine dataset comprising 60 patients (selected from Chinese National Stroke Registry, CNSR) and a research dataset composed of 60 patients (selected from MICCAI WMH Challenge, MWC). The performance of our pipeline was compared with four freely available methods: LGA, LPA, UBO detector, and U-Net, in terms of a variety of metrics. Additionally, to access the model generalization ability, another research dataset comprising 40 patients (from Older Australian Twins Study and Sydney Memory and Aging Study, OSM), was selected and tested. The pipeline achieved the best performance in both research dataset and the clinical routine dataset with DSC being significantly higher than other methods (p < .001), reaching .833 and .783, respectively. The results of model generalization ability showed that the model trained on the research dataset (DSC = 0.736) performed higher than that trained on the clinical dataset (DSC = 0.622). Our method outperformed widely used pipelines in WMHs segmentation. This system could generate both image and text outputs for whole brain, lobar and anatomical automatic labeling WMHs. Additionally, software and models of our method are made publicly available at https://www.nitrc.org/projects/what_v1.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.