ArticleHuman brain mapping2022
Deep Bayesian networks for uncertainty estimation and adversarial resistance of white matter hyperintensity segmentation.
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 32 papers.
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Who cites it
32 citing papers in PubMed, 49 citations in OpenAlex.
- Independent effects of white matter lesion volume and APOE ɛ4 on ARIA-H in A4 Study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Trial
- Greater White Matter Hyperintensity Volume Is Associated with the Number of Microhemorrhages in Preclinical Alzheimer's Disease.The journal of prevention of Alzheimer's disease · 2024Trial
- Deep learning-based cognitive impairment brain imaging analysis: New methods, new technologies, and new paradigms.Neural regeneration research · 2026Article
- Uncertainty Quantification in Medical Image Segmentation: A Comprehensive Survey.Journal of imaging · 2026Review
- Article
- segcsvdHuman brain mapping · 2026Article
- Artificial intelligence for white matter hyperintensity segmentation: performance, determinants, and clinical implications.Frontiers in aging neuroscience · 2026Article
- White matter hyperintensity modulates the amyloid-tau-cognition association and anti-amyloid treatment efficacy in asymptomatic older adults.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Brain and retina in Alzheimer's disease: Pathological intersections and estimates from imaging.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Review
- 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
- Bayesian Graphical Models for Multiscale Inference in Medical Image-Based Joint Degeneration Analysis.Diagnostics (Basel, Switzerland) · 2025Review
- A deep learning pipeline for three-dimensional brain-wide mapping of local neuronal ensembles in teravoxel light-sheet microscopy.Nature methods · 2025Article
- Technical and clinical validation of a novel deep learning-based white matter hyperintensity segmentation tool.Cerebral circulation - cognition and behavior · 2025Article
- Cerebral small vessel disease lesion segmentation methods: A systematic review.Cerebral circulation - cognition and behavior · 2025Review
- Free water elimination tractometry for aging brains.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- segcsvdHuman brain mapping · 2024Article
- Better cardiovascular health is associated with slowed clinical progression in autosomal dominant frontotemporal lobar degeneration variant carriers.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Article
- Vascular contributions to cognitive decline: Beyond amyloid and tau in the Harvard Aging Brain Study.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2024Article
- Distinct brain morphometry patterns revealed by deep learning improve prediction of post-stroke aphasia severity.Communications medicine · 2024Article
Corrections and comments
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Authors and funding
27 authors at 10 institutions in 2 countries.
Funding
Abstract
White matter hyperintensities (WMHs) are frequently observed on structural neuroimaging of elderly populations and are associated with cognitive decline and increased risk of dementia. Many existing WMH segmentation algorithms produce suboptimal results in populations with vascular lesions or brain atrophy, or require parameter tuning and are computationally expensive. Additionally, most algorithms do not generate a confidence estimate of segmentation quality, limiting their interpretation. MRI-based segmentation methods are often sensitive to acquisition protocols, scanners, noise-level, and image contrast, failing to generalize to other populations and out-of-distribution datasets. Given these concerns, we propose a novel Bayesian 3D convolutional neural network with a U-Net architecture that automatically segments WMH, provides uncertainty estimates of the segmentation output for quality control, and is robust to changes in acquisition protocols. We also provide a second model to differentiate deep and periventricular WMH. Four hundred thirty-two subjects were recruited to train the CNNs from four multisite imaging studies. A separate test set of 158 subjects was used for evaluation, including an unseen multisite study. We compared our model to two established state-of-the-art techniques (BIANCA and DeepMedic), highlighting its accuracy and efficiency. Our Bayesian 3D U-Net achieved the highest Dice similarity coefficient of 0.89 ± 0.08 and the lowest modified Hausdorff distance of 2.98 ± 4.40 mm. We further validated our models highlighting their robustness on "clinical adversarial cases" simulating data with low signal-to-noise ratio, low resolution, and different contrast (stemming from MRI sequences with different parameters). Our pipeline and models are available at: https://hypermapp3r.readthedocs.io.
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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.