ReviewJournal of neuroimaging : official journal of the American Society of Neuroimaging
A Novel Convolutional Neural Network for Automated Multiple Sclerosis Brain Lesion Segmentation.
Review in Journal of neuroimaging : official journal of the American Society of Neuroimaging. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Pseudo-RGB Slice Stacking in 2D ResUNet for High-Sensitivity Multiple Sclerosis Lesion Segmentation.Diagnostics (Basel, Switzerland) · 2026Article
- Norms for Automatic Estimation of White Matter Hyperintensities Burden: LST-AI Service in neuGRID.Diagnostics (Basel, Switzerland) · 2026Article
- Synthetic MRI and radiomics in multiple sclerosis: improving precision in diagnosis and prognosis.Radiological physics and technology · 2026Review
- BrainAgeMS: protocol for a prospective comparative study of brain aging in healthy adults and people with multiple sclerosis.Frontiers in neurology · 2026Observational
- Applications of artificial intelligence in systemic lupus erythematosus: integrating multi-omics data for precision medicine.Frontiers in immunology · 2026Review
- Longitudinal deep learning-based segmentation of multiple sclerosis lesions in a local clinical cohort.Frontiers in radiology · 2026Article
- RAUM-GANs: a multi-layer GAN-enhanced framework for accurate multiple sclerosis lesion segmentation in MRI.Scientific reports · 2025Article
- A Novel Convolutional Neural Network for Automated Multiple Sclerosis Brain Lesion Segmentation.Journal of neuroimaging : official journal of the American Society of NeuroimagingReview
- Review
- DenseLes: slice-wise dense network for multiple sclerosis lesion segmentation and classification.Frontiers in neurologyArticle
Corrections and comments
- Erratum issued
- Update of
Authors and funding
17 authors.
Funding
Abstract
background and purposeAssessment of brain lesions on magnetic resonance imaging (MRI) is crucial for research in multiple sclerosis (MS). Manual segmentation is time-consuming and inconsistent. We aimed to develop an automated MS lesion segmentation algorithm for T2-weighted fluid-attenuated inversion recovery (FLAIR) MRI.
methodsWe developed FLAIR Lesion Analysis in Multiple Sclerosis (FLAMeS), a deep learning-based MS lesion segmentation algorithm based on the nnU-Net 3D full-resolution U-Net and trained on 668 FLAIR 1.5 and 3 tesla scans from persons with MS. FLAMeS was evaluated on three external datasets: MSSEG-2 (n = 14), MSLesSeg (n = 51), and a clinical cohort (n = 10), and compared to SAMSEG, LST-LPA, and LST-AI. Performance was assessed qualitatively by two blinded experts and quantitatively by comparing automated and ground truth lesion masks using standard segmentation metrics.
resultsIn a blinded qualitative review of 20 scans, both raters selected FLAMeS as the most accurate segmentation in 15 cases, with one rater favoring FLAMeS in two additional cases. Across all testing datasets, FLAMeS achieved a mean Dice score of 0.74, a true positive rate of 0.84, and an F1 score of 0.78, consistently outperforming the benchmark methods. For other metrics, including positive predictive value, relative volume difference, and false positive rate, FLAMeS performed similarly to or better than benchmark methods. Most lesions missed by FLAMeS were smaller than 10 mm
conclusionsFLAMeS is an accurate, robust method for MS lesion segmentation that outperforms other publicly available methods.
Indexed as
Identifiers
What OpenQuestion holds
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.