ArticleMedical image analysis2017
ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI.
Article in Medical image analysis, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 111 papers, 1 of them a synthesis that pooled it.
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Who cites it
111 citing papers in PubMed, 1 synthesis or guideline pooled 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
- 3D Deep Neural Network Segmentation of Intracerebral Hemorrhage: Development and Validation for Clinical Trials.Neuroinformatics · 2021Trial
- Beyond the LUMIR challenge: The pathway to foundational registration models.Medical image analysis · 2026Article
- Automatic Segmentation of Ischaemic Stroke Lesions Using Transformers and Convolutional Neural Networks Applied to Multimodal Neuroimaging.Sensors (Basel, Switzerland) · 2026Article
- Dual-mode analysis of ischemic stroke based on urine SERS spectra and carotid B-ultrasound.Science advances · 2026Article
- Deep learning in acute ischemic stroke imaging: a systematic review of CT- and MRI-based segmentation, triage, and prognostic modeling.Neuroradiology · 2026Review
- Attention U-Net with differential privacy in federated learning framework for brain stroke lesion segmentation.Scientific reports · 2026Article
- Cloud-to-Edge Deployment of Optimized nnU-Net for Ischemic Stroke Lesion Segmentation on Resource-Constrained Embedded Devices.Sensors (Basel, Switzerland) · 2026Article
- BONBID-HIE 2023: Lesion Segmentation Challenge in BOston Neonatal Brain Injury Data for Hypoxic Ischemic Encephalopathy.IEEE transactions on medical imaging · 2026Article
- LBMNet: a hybrid multi-scale CNN-Mamba framework for enhanced 3D stroke lesion segmentation in MRI.Frontiers in medicine · 2026Article
- From Lesion to Decision: AI for ARIA Detection and Predictive Imaging in Alzheimer's Disease.Biomedicines · 2025Review
- Utility of Deep Learning to Address Missing Modalities from Multi-Modal Medical Imaging Studies: A Systematic Review.Artificial intelligence and applications (Commerce, Calif.) · 2025Article
- Automated deep U-Net model for ischemic stroke lesion segmentation in the sub-acute phase.Scientific reports · 2025Article
- Optimization of deep learning models for inference in low resource environments.Computers in biology and medicine · 2025Article
- DeepISLES: a clinically validated ischemic stroke segmentation model from the ISLES'22 challenge.Nature communications · 2025Article
- Deep learning to identify stroke within 4.5 h using DWI and FLAIR in a prospective multicenter study.Scientific reports · 2025Article
- The BraTS-Africa Dataset: Expanding the Brain Tumor Segmentation Data to Capture African Populations.Radiology. Artificial intelligence · 2025Article
- PARADISE: Personalized and regional adaptation for HIE disease identification and segmentation.Medical image analysis · 2025Article
- Automatic prediction of stroke treatment outcomes: latest advances and perspectives.Biomedical engineering letters · 2025Review
- BOston Neonatal Brain Injury Data for Hypoxic Ischemic Encephalopathy (BONBID-HIE): I. MRI and Lesion Labeling.Scientific data · 2025Article
51 more citing papers are in PubMed but not listed here.
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49 authors.
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Abstract
Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org).
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