ArticleNeuroImage. Clinical2018
Partial volume-aware assessment of multiple sclerosis lesions.
Article in NeuroImage. Clinical, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 18 citations in OpenAlex.
- Generalizable spinal cord multiple sclerosis lesion segmentation across MRI contrasts, protocols, and centers.Multiple sclerosis (Houndmills, Basingstoke, England) · 2026Article
- Instance-level quantitative saliency in multiple sclerosis lesion segmentation.Scientific reports · 2026Article
- Cross-Sectional Validation of an Automated Lesion Segmentation Software in Multiple Sclerosis: Comparison with Radiologist Assessments.AJNR. American journal of neuroradiology · 2025Article
- Automated Quantitative Susceptibility and Morphometry MR Study: Feasibility and Interrelation Between Clinical Score, Lesion Load, Deep Grey Matter and Normal-Appearing White Matter in Multiple Sclerosis.Diagnostics (Basel, Switzerland) · 2024Article
- Quantifying Remyelination Using χ-Separation in White Matter and Cortical Multiple Sclerosis Lesions.Neurology · 2024Article
- Microstructural characterization of multiple sclerosis lesion phenotypes using multiparametric longitudinal analysis.Journal of neurology · 2024Article
- Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review.BMJ open · 2021Article
- Serum GFAP in multiple sclerosis: correlation with disease type and MRI markers of disease severity.Scientific reports · 2020Article
- Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence.NeuroImage. Clinical · 2020Article
- Longitudinal analysis of white matter and cortical lesions in multiple sclerosis.NeuroImage. Clinical · 2019Article
Corrections and comments
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Authors and funding
9 authors at 7 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
White-matter lesion count and volume estimation are key to the diagnosis and monitoring of multiple sclerosis (MS). Automated MS lesion segmentation methods that have been proposed in the past 20 years reach their limits when applied to patients in early disease stages characterized by low lesion load and small lesions. We propose an algorithm to automatically assess MS lesion load (number and volume) while taking into account the mixing of healthy and lesional tissue in the image voxels due to partial volume effects. The proposed method works on 3D MPRAGE and 3D FLAIR images as obtained from current routine MS clinical protocols. The method was evaluated and compared with manual segmentation on a cohort of 39 early-stage MS patients with low disability, and showed higher Dice similarity coefficients (median DSC = 0.55) and higher detection rate (median DR = 61%) than two widely used methods (median DSC = 0.50, median DR < 45%) for automated MS lesion segmentation. We argue that this is due to the higher performance in segmentation of small lesions, which are inherently prone to partial volume effects.
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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.