ArticleBrain sciences2021
Semi-Supervised Learning in Medical MRI Segmentation: Brain Tissue with White Matter Hyperintensity Segmentation Using FLAIR MRI.
Article in Brain sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Commercial volumetric MRI reporting tools in multiple sclerosis: a systematic review of the evidence.Neuroradiology · 2023Pooled it
- Prediction of Post-Infectious Inflammatory Response Syndrome in Patients with Cryptococcal Meningitis Based on Clinical and Radiomics Data.Diagnostics (Basel, Switzerland) · 2026Article
- Deep optimization-guided hybrid neural network for accurate detection and segmentation of white matter hyperintensities in clinical MRI images.Scientific reports · 2026Article
- Artificial intelligence for white matter hyperintensity segmentation: performance, determinants, and clinical implications.Frontiers in aging neuroscience · 2026Article
- Multi-center evaluation of Neurophet AQUA for brain MRI segmentation: T1 compared with FreeSurfer and T2-FLAIR compared with ground truth.Frontiers in neurology · 2025Article
- Impact of Systolic Blood Viscosity on Deep White Matter Hyperintensities in Patients With Acute Ischemic Stroke.Journal of the American Heart Association · 2024Article
- Development of a prediction model for cognitive impairment of sarcopenia using multimodal neuroimaging in non-demented older adults.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Application of artificial intelligence-based magnetic resonance imaging in diagnosis of cerebral small vessel disease.CNS neuroscience & therapeutics · 2024Review
- Specialized gray matter segmentation via a generative adversarial network: application on brain white matter hyperintensities classification.Frontiers in neuroscience · 2024Article
- Multi-stage semi-supervised learning enhances white matter hyperintensity segmentation.Frontiers in computational neuroscience · 2024Article
- Quantitative Analysis of Multimodal MRI Markers and Clinical Risk Factors for Cerebral Small Vessel Disease Based on Deep Learning.International journal of general medicine · 2024Article
- Magnetic resonance image-based brain age as a discriminator of dementia conversion in patients with amyloid-negative amnestic mild cognitive impairment.Scientific reports · 2023Article
- Cortical Thickness and White Matter Hyperintensity Changes Are Associated With Sarcopenia in the Cognitively Normal Older Adults.Psychiatry investigation · 2022Article
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Authors and funding
11 authors.
Funding
Abstract
White-matter hyperintensity (WMH) is a primary biomarker for small-vessel cerebrovascular disease, Alzheimer's disease (AD), and others. The association of WMH with brain structural changes has also recently been reported. Although fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) provide valuable information about WMH, FLAIR does not provide other normal tissue information. The multi-modal analysis of FLAIR and T1-weighted (T1w) MRI is thus desirable for WMH-related brain aging studies. In clinical settings, however, FLAIR is often the only available modality. In this study, we thus propose a semi-supervised learning method for full brain segmentation using FLAIR. The results of our proposed method were compared with the reference labels, which were obtained by FreeSurfer segmentation on T1w MRI. The relative volume difference between the two sets of results shows that our proposed method has high reliability. We further evaluated our proposed WMH segmentation by comparing the Dice similarity coefficients of the reference and the results of our proposed method. We believe our semi-supervised learning method has a great potential for use for other MRI sequences and will encourage others to perform brain tissue segmentation using MRI modalities other than T1w.
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