ArticleNeuroImage2021
White matter hyperintensities segmentation using the ensemble U-Net with multi-scale highlighting foregrounds.
Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 3 of them syntheses that pooled it.
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
35 citing papers in PubMed, 3 syntheses or guidelines pooled it, 65 citations in OpenAlex.
- Machine learning applications in vascular neuroimaging for the diagnosis and prognosis of cognitive impairment and dementia: a systematic review and meta-analysis.Alzheimer's research & therapy · 2025Pooled it
- A 25-Year Retrospective of the Use of AI for Diagnosing Acute Stroke: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Exploring approaches to tackle cross-domain challenges in brain medical image segmentation: a systematic review.Frontiers in neuroscience · 2024Pooled it
- 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
- Multimodal Free-Water Imaging Links Cardiometabolic Risk to Periarterial Dysfunction and Amyloid Accumulation in Early Alzheimer's.Research square · 2026Article
- Interpretable machine learning for cognitive impairment assessment: integration of clinical and radiomic white matter hyperintensities features.Journal of translational medicine · 2026Article
- Masked autoencoding, generalizable pretraining, and integrated experts for enhanced glioma segmentation.NPJ digital medicine · 2026Article
- Multimodal Magnetic Resonance Imaging Signatures of White Matter Hyperintensities: Mechanistic Insights Into Pathobiological Heterogeneity.Journal of stroke · 2026Review
- Comparison of automated white matter lesion segmentation approaches for use in large, multi-site data analyses in Parkinson's disease.Frontiers in neuroscience · 2026Article
- Alzheimer's disease detection from structural brain MRI using FFA U-Net segmentation and transfer learning.Frontiers in neuroscience · 2026Article
- Associations between cerebral blood transit time, amyloid-β pathology and cognitive decline in non-demented older adults.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Advantage of grading classification using volumetric artificial intelligence for periventricular hyperintensity and deep subcortical white matter hyperintensity.Scientific reports · 2025Article
- The differential relationship between white matter cerebrovascular perfusion and microstructure between normal cognition and mild cognitive impairment.Journal of Alzheimer's disease : JAD · 2025Article
- Distinct spatiotemporal patterns of white matter hyperintensity progression.Nature communications · 2025Article
- Implication of dopamine transporter and electroencephalography biomarkers in dementia with lewy bodies.Scientific reports · 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
- Clinical correlates of data-driven subtypes of deep gray matter atrophy and dopamine availability in early Parkinson's disease.NPJ Parkinson's disease · 2025Article
- WMH-DualTasker: A Weakly Supervised Deep Learning Model for Automated White Matter Hyperintensities Segmentation and Visual Rating Prediction.Human brain mapping · 2025Article
- Automatic segmentation of white matter lesions on multi-parametric MRI: convolutional neural network versus vision transformer.BMC neurology · 2025Article
- Technical and clinical validation of a novel deep learning-based white matter hyperintensity segmentation tool.Cerebral circulation - cognition and behavior · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
White matter hyperintensities (WMHs) are abnormal signals within the white matter region on the human brain MRI and have been associated with aging processes, cognitive decline, and dementia. In the current study, we proposed a U-Net with multi-scale highlighting foregrounds (HF) for WMHs segmentation. Our method, U-Net with HF, is designed to improve the detection of the WMH voxels with partial volume effects. We evaluated the segmentation performance of the proposed approach using the Challenge training dataset. Then we assessed the clinical utility of the WMH volumes that were automatically computed using our method and the Alzheimer's Disease Neuroimaging Initiative database. We demonstrated that the U-Net with HF significantly improved the detection of the WMH voxels at the boundary of the WMHs or in small WMH clusters quantitatively and qualitatively. Up to date, the proposed method has achieved the best overall evaluation scores, the highest dice similarity index, and the best F1-score among 39 methods submitted on the WMH Segmentation Challenge that was initially hosted by MICCAI 2017 and is continuously accepting new challengers. The evaluation of the clinical utility showed that the WMH volume that was automatically computed using U-Net with HF was significantly associated with cognitive performance and improves the classification between cognitive normal and Alzheimer's disease subjects and between patients with mild cognitive impairment and those with Alzheimer's disease. The implementation of our proposed method is publicly available using Dockerhub (https://hub.docker.com/r/wmhchallenge/pgs).
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.