ArticleFrontiers in neurology2019
Stroke Lesion Segmentation in FLAIR MRI Datasets Using Customized Markov Random Fields.
Article in Frontiers in neurology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Deep learning-based cognitive impairment brain imaging analysis: New methods, new technologies, and new paradigms.Neural regeneration research · 2026Article
- Quantum SVM-driven framework for accurate brain stroke classification.Scientific reports · 2026Article
- Automated deep U-Net model for ischemic stroke lesion segmentation in the sub-acute phase.Scientific reports · 2025Article
- A dual-stage framework for segmentation of the brain anatomical regions with high accuracy.Magma (New York, N.Y.) · 2025Article
- [Ischemic stroke infarct segmentation model based on depthwise separable convolution for multimodal magnetic resonance imaging].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Artificial Intelligence for Automated DWI/FLAIR Mismatch Assessment on Magnetic Resonance Imaging in Stroke: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Machine learning based analysis of stroke lesions on mouse tissue sections.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2022Article
- Artificial intelligence for localization of the acute ischemic stroke by non-contrast computed tomography.PloS one · 2022Article
- Radiomics in Stroke Neuroimaging: Techniques, Applications, and Challenges.Aging and disease · 2021Review
- RatLesNetv2: A Fully Convolutional Network for Rodent Brain Lesion Segmentation.Frontiers in neuroscience · 2020Article
- Cortical Thickness Estimation in Individuals With Cerebral Small Vessel Disease, Focal Atrophy, and Chronic Stroke Lesions.Frontiers in neuroscience · 2020Article
- Improved multi-parametric prediction of tissue outcome in acute ischemic stroke patients using spatial features.PloS one · 2020Article
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7 authors.
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Abstract
Robust and reliable stroke lesion segmentation is a crucial step toward employing lesion volume as an independent endpoint for randomized trials. The aim of this work was to develop and evaluate a novel method to segment sub-acute ischemic stroke lesions from fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) datasets. After preprocessing of the datasets, a Bayesian technique based on Gabor textures extracted from the FLAIR signal intensities is utilized to generate a first estimate of the lesion segmentation. Using this initial segmentation, a customized voxel-level Markov random field model based on intensity as well as Gabor texture features is employed to refine the stroke lesion segmentation. The proposed method was developed and evaluated based on 151 multi-center datasets from three different databases using a leave-one-patient-out validation approach. The comparison of the automatically segmented stroke lesions with manual ground truth segmentation revealed an average Dice coefficient of 0.582, which is in the upper range of previously presented lesion segmentation methods using multi-modal MRI datasets. Furthermore, the results obtained by the proposed technique are superior compared to the results obtained by two methods based on convolutional neural networks and three phase level-sets, respectively, which performed best in the ISLES 2015 challenge using multi-modal imaging datasets. The results of the quantitative evaluation suggest that the proposed method leads to robust lesion segmentation results using FLAIR MRI datasets only as a follow-up sequence.
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