ArticleNeuroImage. Clinical2020
Multiple sclerosis cortical and WM lesion segmentation at 3T MRI: a deep learning method based on FLAIR and MP2RAGE.
Article in NeuroImage. Clinical, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 1 of them a synthesis that pooled it.
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Who cites it
66 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI.Frontiers in neuroinformatics · 2020Pooled it
- Betaine Attenuates Cuprizone-Induced Cortical Demyelination: The Role of the WNT4/mTOR Signalling Pathway in Regulating Astrogliosis and Synaptic Integrity.Journal of biochemical and molecular toxicology · 2026Article
- Automated Deep Learning-Based Demyelination Load Segmentation in Metachromatic Leukodystrophy.Clinical neuroradiology · 2026Article
- Multi-compartment Myelin Streamline Decomposition reveals tract-specific and global brain network myelination in Multiple Sclerosis.Neuroimage. Reports · 2026Article
- Quantifying cortical lesions in multiple sclerosis MRI datasets using multi-contrast post-processing and deep learning.Communications medicine · 2026Article
- UNISELF: A unified network with instance normalization and self-ensembled lesion fusion for multiple sclerosis lesion segmentation.Medical image analysis · 2026Article
- Instance-level quantitative saliency in multiple sclerosis lesion segmentation.Scientific reports · 2026Article
- Assessing the Relative Importance of Imaging and Serum Biomarkers in Capturing Disability, Cognitive Impairment, and Clinical Progression in Multiple Sclerosis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Longitudinal deep learning-based segmentation of multiple sclerosis lesions in a local clinical cohort.Frontiers in radiology · 2026Article
- A comparative study of deep learning for cortical lesion MRI segmentation with explainability analysis in multiple sclerosis.NeuroImage. Clinical · 2026Article
- Integrating big data and artificial intelligence to predict progression in multiple sclerosis: challenges and the path forward.Journal of neuroengineering and rehabilitation · 2025Review
- Unraveling Microstructural and Macrostructural Brain Age Dynamics in Multiple Sclerosis.Neurology(R) neuroimmunology & neuroinflammation · 2025Article
- Article
- 7 Tesla MRI in Multiple Sclerosis: Insights From Its Use in Clinical Routine.European journal of neurology · 2025Article
- Benchmarking and Explaining Deep Learning Cortical Lesion MRI Segmentation in Multiple Sclerosis.ArXiv · 2025Article
- Comparative effectiveness of teriflunomide and ocrelizumab on smoldering activity in multiple sclerosis: an observational study in the Swiss Multiple Sclerosis Cohort.Journal of neurology · 2025Observational
- Explaining Uncertainty in Multiple Sclerosis Lesion Segmentation Beyond Prediction Errors.ArXiv · 2025Article
- Enhancing precision in multiple sclerosis lesion segmentation: A U-net based machine learning approach with data augmentation.Neuroimage. Reports · 2025Article
- Conventional and Advanced Magnetic Resonance Imaging Biomarkers of Multiple Sclerosis in the Brain.Cureus · 2025Review
- TIME: Tractography-Informed myelin estimation.NeuroImage. Clinical · 2025Article
6 more citing papers are in PubMed but not listed here.
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The presence of cortical lesions in multiple sclerosis patients has emerged as an important biomarker of the disease. They appear in the earliest stages of the illness and have been shown to correlate with the severity of clinical symptoms. However, cortical lesions are hardly visible in conventional magnetic resonance imaging (MRI) at 3T, and thus their automated detection has been so far little explored. In this study, we propose a fully-convolutional deep learning approach, based on the 3D U-Net, for the automated segmentation of cortical and white matter lesions at 3T. For this purpose, we consider a clinically plausible MRI setting consisting of two MRI contrasts only: one conventional T2-weighted sequence (FLAIR), and one specialized T1-weighted sequence (MP2RAGE). We include 90 patients from two different centers with a total of 728 and 3856 gray and white matter lesions, respectively. We show that two reference methods developed for white matter lesion segmentation are inadequate to detect small cortical lesions, whereas our proposed framework is able to achieve a detection rate of 76% for both cortical and white matter lesions with a false positive rate of 29% in comparison to manual segmentation. Further results suggest that our framework generalizes well for both types of lesion in subjects acquired in two hospitals with different scanners.
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