ArticleNeuroImage2024
Accuracy of TrUE-Net in comparison to established white matter hyperintensity segmentation methods: An independent validation study.
Article in NeuroImage, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 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
- Diffusion Basis Spectrum Imaging of Tissue Microstructure and Early Treatment in Multiple Sclerosis.Annals of clinical and translational neurology · 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
- Association between cerebrovascular imaging markers and glymphatic function in a large-scale cognitively normal population.Neuroradiology · 2026Article
- CycleGAN models show consistent brain MRI synthesis across datasets supporting downstream tissue characterization in multiple sclerosis.Frontiers in neuroinformatics · 2026Article
- Introducing the SAGE study: a multimodal protocol for testing a GABAergic mechanism of age-related episodic memory impairment across the sexes.BMJ neurology open · 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
- Pulse Pressure, White Matter Hyperintensities, and Cognition: Mediating Effects Across the Adult Lifespan.Annals of clinical and translational neurology · 2025Article
- Cerebrovascular markers of WMH and infarcts in ADNI: A historical perspective and future directions.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Review
- Features predicting data exclusion in imaging studies of Alzheimer's disease.Alzheimer's & dementia (Amsterdam, Netherlands)Article
Corrections and comments
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Authors and funding
16 authors at 5 institutions in 2 countries.
Funding
Abstract
White matter hyperintensities (WMH) are nearly ubiquitous in the aging brain, and their topography and overall burden are associated with cognitive decline. Given their numerosity, accurate methods to automatically segment WMH are needed. Recent developments, including the availability of challenge data sets and improved deep learning algorithms, have led to a new promising deep-learning based automated segmentation model called TrUE-Net, which has yet to undergo rigorous independent validation. Here, we compare TrUE-Net to six established automated WMH segmentation tools, including a semi-manual method. We evaluated the techniques at both global and regional level to compare their ability to detect the established relationship between WMH burden and age. We found that TrUE-Net was highly reliable at identifying WMH regions with low false positive rates, when compared to semi-manual segmentation as the reference standard. TrUE-Net performed similarly or favorably when compared to the other automated techniques. Moreover, TrUE-Net was able to detect relationships between WMH and age to a similar degree as the reference standard semi-manual segmentation at both the global and regional level. These results support the use of TrUE-Net for identifying WMH at the global or regional level, including in large, combined datasets.
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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.