ArticleNeuroImage2017
Longitudinal multiple sclerosis lesion segmentation: Resource and challenge.
Article in NeuroImage, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 115 papers, 3 of them syntheses 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
115 citing papers in PubMed, 3 syntheses or guidelines pooled it, 344 citations in OpenAlex.
- Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis - a systematic review.Frontiers in neuroscience · 2024Pooled it
- Commercial volumetric MRI reporting tools in multiple sclerosis: a systematic review of the evidence.Neuroradiology · 2023Pooled it
- Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI.Frontiers in neuroinformatics · 2020Pooled it
- Brain and lesion segmentation in multiple sclerosis using fully convolutional neural networks: A large-scale study.Multiple sclerosis (Houndmills, Basingstoke, England) · 2020Trial
- Beyond the LUMIR challenge: The pathway to foundational registration models.Medical image analysis · 2026Article
- A Robust Dual-Stage Learning-Based Pipeline for Multiclass Segmentation of Multiple Sclerosis Lesions in MRI.Diagnostics (Basel, Switzerland) · 2026Article
- Performances of experts and automated methods on new multiple sclerosis lesions detection: insights from the MSSeg2 challenge.Scientific reports · 2026Article
- Reliable Uncertainty Under Class Imbalance and Distribution Shift: Class-Conditional Conformal Prediction of Multiple Sclerosis.medRxiv : the preprint server for health sciences · 2026Article
- Diagnosis of Multiple Sclerosis Using Multimodal Deep Learning Integrating Lesion and Normal-Appearing White Matter: A Retrospective Study with International Multicentre External Validation.medRxiv : the preprint server for health sciences · 2026Article
- A Multiple Sclerosis MRI Dataset with Tri-Mask Annotations for Lesion Segmentation.Scientific data · 2026Article
- Reliability of a convolutional neural network in segmenting multiple sclerosis lesions from MRI: Impact of data augmentation, image modality and tolerance with U-Net architecture.PLOS digital health · 2026Article
- Improving Neuropathological Reconstruction Fidelity via AI Slice Imputation.Research square · 2026Article
- UNISELF: A unified network with instance normalization and self-ensembled lesion fusion for multiple sclerosis lesion segmentation.Medical image analysis · 2026Article
- CalDiff: Calibrating Uncertainty and Accessing Reliability of Diffusion Models for Trustworthy Lesion Segmentation.IEEE journal of biomedical and health informatics · 2026Article
- Multi-scale Gaussian product-of-experts for missing sequence multiple sclerosis lesion segmentation.Frontiers in radiology · 2026Article
- Longitudinal deep learning-based segmentation of multiple sclerosis lesions in a local clinical cohort.Frontiers in radiology · 2026Article
- Adaptive multi-stage domain unlearning for white-matter lesion segmentation.Frontiers in medicine · 2026Article
- Self-supervised brain lesion generation for effective data augmentation of medical images.Neural networks : the official journal of the International Neural Network Society · 2025Article
- Article
- PediMS: A Pediatric Multiple Sclerosis Lesion Segmentation Dataset.Scientific data · 2025Article
55 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
46 authors at 17 institutions in 11 countries.
Funding
Abstract
In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website
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.