ArticleScientific reports2024
Consensus of algorithms for lesion segmentation in brain MRI studies of multiple sclerosis.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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, 1 synthesis or guideline pooled it.
- Spinal cord imaging in multiple sclerosis: A vital component we can no longer overlook.Multiple sclerosis (Houndmills, Basingstoke, England) · 2026Pooled it
- Deep Learning for Automated Detection and Segmentation of Meningioma on Multiparametric MRI.Journal of imaging informatics in medicine · 2026Article
- An automated quantitative report for multiple sclerosis using only 3D T2-fluid-attenuated inversion recovery MRI.Neuroradiology · 2026Article
- Instance-level quantitative saliency in multiple sclerosis lesion segmentation.Scientific reports · 2026Article
- Regularization via gradient attribution for multiple sclerosis lesion segmentation.Frontiers in medicine · 2026Article
- A multi-center study of transformer-based CNNs for multiple sclerosis lesion segmentation on 3D FLAIR MRI.Neuroradiology · 2025Article
- Semi-supervised Label Generation for 3D Multi-modal MRI Bone Tumor Segmentation.Journal of imaging informatics in medicine · 2025Article
- HeteroMRI: Robust white matter abnormality classification across multi-scanner MRI data.GigaScience · 2025Article
- Unsupervised semi-automated MRI segmentation detects cortical lesion expansion in chronic traumatic brain injury.Frontiers in neurology · 2025Article
- Multiple sclerosis: 2024 update.Free neuropathology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Segmentation of multiple sclerosis (MS) lesions on brain MRI scans is crucial for diagnosis, disease and treatment monitoring but is a time-consuming task. Despite several automated algorithms have been proposed, there is still no consensus on the most effective method. Here, we applied a consensus-based framework to improve lesion segmentation on T1-weighted and FLAIR scans. The framework is designed to combine publicly available state-of-the-art deep learning models, by running multiple segmentation tasks before merging the outputs of each algorithm. To assess the effectiveness of the approach, we applied it to MRI datasets from two different centers, including a private and a public dataset, with 131 and 30 MS patients respectively, with manually segmented lesion masks available. No further training was performed for any of the included algorithms. Overlap and detection scores were improved, with Dice increasing by 4-8% and precision by 3-4% respectively for the private and public dataset. High agreement was obtained between estimated and true lesion load (ρ = 0.92 and ρ = 0.97) and count (ρ = 0.83 and ρ = 0.94). Overall, this framework ensures accurate and reliable results, exploiting complementary features and overcoming some of the limitations of individual algorithms.
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.