ArticleFrontiers in aging neuroscience2022
Automatic segmentation of white matter hyperintensities in routine clinical brain MRI by 2D VB-Net: A large-scale study.
Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed, 52 citations in OpenAlex.
- Multimodal MRI assessment of cerebral small vessel disease-related brain injury in the context of unilateral severe carotid artery stenosis.Neuroradiology · 2026Article
- Altered glymphatic-related MRI markers in children with acute lymphoblastic and myeloid leukemia at diagnosis.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- MRI In Vivo Detection of Amyloid-β Protein Deposition in Different Brain Regions of Patients with AD and MCI.Brain topography · 2026Article
- Stage-Dependent mediation of white matter hyperintensities between plasma biomarkers and cognitive function in Alzheimer's disease.Translational psychiatry · 2026Article
- Free water in white matter links focal hemodynamic compromise to cognitive impairment in asymptomatic extracranial carotid artery stenosis.BMC medical imaging · 2026Article
- Association of Metabolic Dysfunction-Associated Steatotic Liver Disease With Features of Cerebral Small-Vessel Disease on Magnetic Resonance Images: A Large Cross-Sectional Study.Journal of the American Heart Association · 2026Article
- Glymphatic system dysfunction in pediatric growth hormone deficiency evidenced by multiple MRI metrics.Quantitative imaging in medicine and surgery · 2026Article
- Advantage of grading classification using volumetric artificial intelligence for periventricular hyperintensity and deep subcortical white matter hyperintensity.Scientific reports · 2025Article
- An exploratory study of the diffusion tensor imaging analysis along perivascular spaces (DTI-ALPS) index combined with quantitative analysis of choroid plexus volume and perivascular spaces in different cognitive stages of cerebral small vessel disease.Quantitative imaging in medicine and surgery · 2025Article
- Cerebral Small Vessel Disease Outperforms Brain Atrophy as an Imaging Biomarker in Diabetic Retinopathy.Journal of diabetes · 2025Article
- HeteroMRI: Robust white matter abnormality classification across multi-scanner MRI data.GigaScience · 2025Article
- MRI quantified enlarged perivascular space volumes as imaging biomarkers correlating with severity of anxiety depression in young adults with long-time mobile phone use.Frontiers in psychiatry · 2025Article
- Cerebral small vessel disease lesion segmentation methods: A systematic review.Cerebral circulation - cognition and behavior · 2025Review
- Exploration of heterogeneity in risk factors associated with imaging subtypes of white matter hyperintensities on fluid-attenuated inversion recovery magnetic resonance imaging.Frontiers in neurology · 2025Article
- Quantitative assessment of brain glymphatic imaging features using deep learning-based EPVS segmentation and DTI-ALPS analysis in Alzheimer's disease.Frontiers in aging neuroscience · 2025Article
- MRI-based machine learning analysis of perivascular spaces and their link to sleep disturbances, dementia, and mental distress in young adults with long-time mobile phone use.Frontiers in neuroscience · 2025Article
- Brain Iron Deposition Alterations in Type 2 Diabetes Mellitus Patients With Mild Cognitive Impairment Based on Quantitative Susceptibility Mapping.Journal of diabetes · 2025Observational
- Beyond the cerebellum: perivascular space burden in spinocerebellar ataxia type 3 extends to multiple brain regions.Brain communications · 2025Article
- Integrating quantitative DCE-MRI parameters and radiomic features for improved IDH mutation prediction in gliomas.Frontiers in oncology · 2025Article
- Factors associated with the distribution of brain metastases in lung cancer: a retrospective study.Clinical & experimental metastasis · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
White matter hyperintensities (WMH) are imaging manifestations frequently observed in various neurological disorders, yet the clinical application of WMH quantification is limited. In this study, we designed a series of dedicated WMH labeling protocols and proposed a convolutional neural network named 2D VB-Net for the segmentation of WMH and other coexisting intracranial lesions based on a large dataset of 1,045 subjects across various demographics and multiple scanners using 2D thick-slice protocols that are more commonly applied in clinical practice. Using our labeling pipeline, the Dice consistency of the WMH regions manually depicted by two observers was 0.878, which formed a solid basis for the development and evaluation of the automatic segmentation system. The proposed algorithm outperformed other state-of-the-art methods (uResNet, 3D V-Net and Visual Geometry Group network) in the segmentation of WMH and other coexisting intracranial lesions and was well validated on datasets with thick-slice magnetic resonance (MR) images and the 2017 medical image computing and computer assisted intervention WMH Segmentation Challenge dataset (with thin-slice MR images), all showing excellent effectiveness. Furthermore, our method can subclassify WMH to display the WMH distributions and is very lightweight. Additionally, in terms of correlation to visual rating scores, our algorithm showed excellent consistency with the manual delineations and was overall better than those from other competing methods. In conclusion, we developed an automatic WMH quantification framework for multiple application scenarios, exhibiting a promising future in clinical practice.
Indexed as
Identifiers
What OpenQuestion holds
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.