ArticleScientific data2022
ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 2 of them syntheses that pooled it.
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The trial behind it
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Who cites it
56 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep Learning Applications in Imaging of Acute Ischemic Stroke: A Systematic Review and Narrative Summary.Radiology · 2025Pooled it
- Deep learning for MRI-based acute and subacute ischaemic stroke lesion segmentation-a systematic review, meta-analysis, and pilot evaluation of key results.Frontiers in medical technology · 2025Pooled it
- Adapting Segment Anything Model 3 for concept-driven lesion segmentation in medical images: an experimental study.Visual computing for industry, biomedicine, and art · 2026Article
- Automatic Segmentation of Ischaemic Stroke Lesions Using Transformers and Convolutional Neural Networks Applied to Multimodal Neuroimaging.Sensors (Basel, Switzerland) · 2026Article
- Reliability-Aware Semi-supervised Mutual Learning for Acute Ischemic Stroke Lesion Segmentation.Journal of imaging informatics in medicine · 2026Article
- A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning.Scientific data · 2026Article
- Deep learning in acute ischemic stroke imaging: a systematic review of CT- and MRI-based segmentation, triage, and prognostic modeling.Neuroradiology · 2026Review
- Exploiting Cross-modal Collaboration and Discrepancy for Semi-supervised Ischemic Stroke Lesion Segmentation from Multi-sequence MRI Images.Journal of imaging informatics in medicine · 2026Article
- A clinical neuroimaging platform for rapid, automated lesion detection and personalized post-stroke outcome prediction.NPJ digital medicine · 2026Article
- Cloud-to-Edge Deployment of Optimized nnU-Net for Ischemic Stroke Lesion Segmentation on Resource-Constrained Embedded Devices.Sensors (Basel, Switzerland) · 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
- [Prior knowledge-guided and border-focused segmentation of ischemic stroke lesions].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- BONBID-HIE 2023: Lesion Segmentation Challenge in BOston Neonatal Brain Injury Data for Hypoxic Ischemic Encephalopathy.IEEE transactions on medical imaging · 2026Article
- Automated Multi-Modal MRI Segmentation of Stroke Lesions and Corticospinal Tract Integrity for Functional Outcome Prediction.Tomography (Ann Arbor, Mich.) · 2026Article
- StrDiSeg: Adapter-Enhanced DINOv3 for Automated Ischemic Stroke Lesion Segmentation.Bioengineering (Basel, Switzerland) · 2026Article
- Masked autoencoding, generalizable pretraining, and integrated experts for enhanced glioma segmentation.NPJ digital medicine · 2026Article
- Boundary-aware and uncertainty-guided deep learning for multimodal magnetic resonance imaging segmentation of stroke lesions.Frontiers in neurology · 2026Article
- LBMNet: a hybrid multi-scale CNN-Mamba framework for enhanced 3D stroke lesion segmentation in MRI.Frontiers in medicine · 2026Article
- A Structured Review and Quantitative Profiling of Public Brain MRI Datasets for Foundation Model Development.Journal of imaging · 2025Review
- Neuroengineering approaches assessing structural and functional changes of motor descending pathways in stroke.Progress in biomedical engineering (Bristol, England) · 2025Review
Corrections and comments
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Authors and funding
25 authors.
Funding
Abstract
Magnetic resonance imaging (MRI) is an important imaging modality in stroke. Computer based automated medical image processing is increasingly finding its way into clinical routine. The Ischemic Stroke Lesion Segmentation (ISLES) challenge is a continuous effort to develop and identify benchmark methods for acute and sub-acute ischemic stroke lesion segmentation. Here we introduce an expert-annotated, multicenter MRI dataset for segmentation of acute to subacute stroke lesions ( https://doi.org/10.5281/zenodo.7153326 ). This dataset comprises 400 multi-vendor MRI cases with high variability in stroke lesion size, quantity and location. It is split into a training dataset of n = 250 and a test dataset of n = 150. All training data is publicly available. The test dataset will be used for model validation only and will not be released to the public. This dataset serves as the foundation of the ISLES 2022 challenge ( https://www.isles-challenge.org/ ) with the goal of finding algorithmic methods to enable the development and benchmarking of automatic, robust and accurate segmentation methods for ischemic stroke.
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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.