ArticleMedical image analysis2021
Triplanar ensemble U-Net model for white matter hyperintensities segmentation on MR images.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- Structural and social determinants of health shape the impact of neurodegeneration and cerebrovascular pathology on cognitive functioning in black adults.Social science & medicine (1982) · 2026Article
- Correlations between white matter hyperintensities, magnetic resonance imaging findings, and cognition in the oldest old: a multimodal cross-sectional study.GeroScience · 2026Article
- Regional growth rates of white matter hyperintensities are associated with beta-amyloid burden.Neurobiology of aging · 2026Article
- Perivascular fluid clearance links choroid plexus changes to cognitive performance in cerebral small vessel disease.Brain informatics · 2026Article
- Association between cerebrovascular imaging markers and glymphatic function in a large-scale cognitively normal population.Neuroradiology · 2026Article
- Imaging research, diagnosis, and treatment advances of post-stroke cognitive impairment.Frontiers in neuroimaging · 2026Review
- Effect of vascular lesion preprocessing on Brain Intensity AbNormality Classification Algorithm (BIANCA) white matter hyperintensity segmentation.NeuroImage. Clinical · 2026Article
- NeuroAgeFusionNet an ensemble deep learning framework integrating CNN, transformers, and GNN for robust brain age estimation using MRI scans.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
- Pulse Pressure, White Matter Hyperintensities, and Cognition: Mediating Effects Across the Adult Lifespan.Annals of clinical and translational neurology · 2025Article
- Enhancing the Predictions of Cytomegalovirus Infection in Severe Ulcerative Colitis Using a Deep Learning Ensemble Model: Development and Validation Study.JMIR medical informatics · 2025Article
- WMH-DualTasker: A Weakly Supervised Deep Learning Model for Automated White Matter Hyperintensities Segmentation and Visual Rating Prediction.Human brain mapping · 2025Article
- Extracting organs of interest from medical images based on convolutional neural network with auxiliary and refined constraints.Scientific reports · 2025Article
- Automatic segmentation of white matter lesions on multi-parametric MRI: convolutional neural network versus vision transformer.BMC neurology · 2025Article
- Deep learning-based prediction of cerebral white matter hyperintensity burden using carotid magnetic resonance angiography.Frontiers in neurology · 2025Article
- Deep learning-based automated segmentation for the quantitative diagnosis of cerebral small vessel disease via multisequence MRI.Frontiers in neurology · 2025Article
- Self-supervised segmentation and characterization of fiber bundles in anatomic tracing data.Imaging neuroscience (Cambridge, Mass.) · 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
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5 authors.
Funding
Abstract
White matter hyperintensities (WMHs) have been associated with various cerebrovascular and neurodegenerative diseases. Reliable quantification of WMHs is essential for understanding their clinical impact in normal and pathological populations. Automated segmentation of WMHs is highly challenging due to heterogeneity in WMH characteristics between deep and periventricular white matter, presence of artefacts and differences in the pathology and demographics of populations. In this work, we propose an ensemble triplanar network that combines the predictions from three different planes of brain MR images to provide an accurate WMH segmentation. In the loss functions the network uses anatomical information regarding WMH spatial distribution in loss functions, to improve the efficiency of segmentation and to overcome the contrast variations between deep and periventricular WMHs. We evaluated our method on 5 datasets, of which 3 are part of a publicly available dataset (training data for MICCAI WMH Segmentation Challenge 2017 - MWSC 2017) consisting of subjects from three different cohorts, and we also submitted our method to MWSC 2017 to be evaluated on the unseen test datasets. On evaluating our method separately in deep and periventricular regions, we observed robust and comparable performance in both regions. Our method performed better than most of the existing methods, including FSL BIANCA, and on par with the top ranking deep learning methods of MWSC 2017.
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