ArticleNeuroradiology2015
A toolbox for multiple sclerosis lesion segmentation.
Article in Neuroradiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- FLAIRNeuroImage. Clinical · 2019Trial
- Quantifying White Matter Hyperintensities: Automated Volumetry Compared with Visual Grading Scales.Medicina (Kaunas, Lithuania) · 2025Article
- Cerebral small vessel disease lesion segmentation methods: A systematic review.Cerebral circulation - cognition and behavior · 2025Review
- Mitigating catastrophic forgetting in Multiple sclerosis lesion segmentation using elastic weight consolidation.NeuroImage. Clinical · 2025Article
- Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review.Royal Society open science · 2025Review
- Real-world experience of teriflunomide in relapsing multiple sclerosis: paramagnetic rim lesions may play a role.Frontiers in immunology · 2024Observational
- Early regional cerebral grey matter damage predicts long-term cognitive impairment phenotypes in multiple sclerosis: a 20-year study.Brain communications · 2024Article
- BIANCA-MS: An optimized tool for automated multiple sclerosis lesion segmentation.Human brain mapping · 2023Article
- Relationship between paramagnetic rim lesions and slowly expanding lesions in multiple sclerosis.Multiple sclerosis (Houndmills, Basingstoke, England) · 2023Article
- Infratentorial lesions in multiple sclerosis patients: intra- and inter-rater variability in comparison to a fully automated segmentation using 3D convolutional neural networks.European radiology · 2022Article
- Detection of subtle white matter lesions in MRI through texture feature extraction and boundary delineation using an embedded clustering strategy.Scientific reports · 2022Article
- Automatic segmentation of white matter hyperintensities: validation and comparison with state-of-the-art methods on both Multiple Sclerosis and elderly subjects.NeuroImage. Clinical · 2022Article
- Multiple Sclerosis Lesions Segmentation Using Attention-Based CNNs in FLAIR Images.IEEE journal of translational engineering in health and medicine · 2022Article
- Reproducibility of Lesion Count in Various Subregions on MRI Scans in Multiple Sclerosis.Frontiers in neurology · 2022Article
- Urinary sodium and potassium excretion and cerebrovascular health: a multimodal imaging study.European journal of nutrition · 2021Article
- Evaluation of Ultrafast Wave-Controlled Aliasing in Parallel Imaging 3D-FLAIR in the Visualization and Volumetric Estimation of Cerebral White Matter Lesions.AJNR. American journal of neuroradiology · 2021Article
- Article
- Radius-optimized efficient template matching for lesion detection from brain images.Scientific reports · 2021Article
- Arterial elasticity, endothelial function and intracranial vascular health: A multimodal MRI study.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2021Article
- Uncontrolled hypertension associates with subclinical cerebrovascular health globally: a multimodal imaging study.European radiology · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionLesion segmentation plays an important role in the diagnosis and follow-up of multiple sclerosis (MS). This task is very time-consuming and subject to intra- and inter-rater variability. In this paper, we present a new tool for automated MS lesion segmentation using T1w and fluid-attenuated inversion recovery (FLAIR) images.
methodsOur approach is based on two main steps, initial brain tissue segmentation according to the gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) performed in T1w images, followed by a second step where the lesions are segmented as outliers to the normal apparent GM brain tissue on the FLAIR image.
resultsThe tool has been validated using data from more than 100 MS patients acquired with different scanners and at different magnetic field strengths. Quantitative evaluation provided a better performance in terms of precision while maintaining similar results on sensitivity and Dice similarity measures compared with those of other approaches.
conclusionOur tool is implemented as a publicly available SPM8/12 extension that can be used by both the medical and research communities.
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