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ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset

arXiv 2206.06694 ↗First posted 14 Jun 2022cs.CVcited by 106

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Abstract (arXiv)

Magnetic resonance imaging (MRI) is a central modality for stroke imaging. It is used upon patient admission to make treatment decisions such as selecting patients for intravenous thrombolysis or endovascular therapy. MRI is later used in the duration of hospital stay to predict outcome by visualizing infarct core size and location. Furthermore, it may be used to characterize stroke etiology, e.g. differentiation between (cardio)-embolic and non-embolic stroke. Computer based automated medical image processing is increasingly finding its way into clinical routine. Previous iterations of the Ischemic Stroke Lesion Segmentation (ISLES) challenge have aided in the generation of identifying 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. 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 will be made 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 with the goal of finding algorithmic methods to enable the development and benchmarking of robust and accurate segmentation algorithms for ischemic stroke.

Key facts. Cited by 106 papers in the map (data through 14 Oct 2026). Builds on 5 of its 58 references that are in the map. First posted on arXiv 14 Jun 2022.

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5 of 58 references.

References · 5

  1. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
  2. 2019Automated brain extraction of multi-sequence MRI using artificial neural networkscited by 134
  3. 2018Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learningcited by 12
  4. 2020AIFNet: Automatic Vascular Function Estimation for Perfusion Analysis Using Deep Learningcited by 5
  5. 2021Differentiable Deconvolution for Improved Stroke Perfusion Analysiscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 13; 2024: 27; 2025: 34; 2026: 29.

106 papers.

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  1. 2024OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLMcited by 184
  2. 2023On the Challenges and Perspectives of Foundation Models for Medical Image Analysiscited by 135
  3. 2023UniverSeg: Universal Medical Image Segmentationcited by 91
  4. 2023Large-Vocabulary Segmentation for Medical Images with Text Promptscited by 62
  5. 2023SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million maskscited by 55
  6. 2023ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Imagecited by 48
  7. 2023One-Prompt to Segment All Medical Imagescited by 38
  8. 2024Revisiting MAE pre-training for 3D medical image segmentationcited by 35
  9. 2023MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Visioncited by 32
  10. 2024How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Modelcited by 32
  11. 2024An OpenMind for 3D medical vision self-supervised learningcited by 26
  12. 2023Neuralizer: General Neuroimage Analysis without Re-Trainingcited by 24
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  1. 2024AutoRG-Brain: Grounded Report Generation for Brain MRIcited by 24
  2. 2024Multi-modal Vision Pre-training for Medical Image Analysiscited by 23
  3. 2024Tyche: Stochastic In-Context Learning for Medical Image Segmentationcited by 20
  4. 2024AMAES: Augmented Masked Autoencoder Pretraining on Public Brain MRI Data for 3D-Native Segmentationcited by 17
  5. 2025MIMO: A medical vision language model with visual referring multimodal input and pixel grounding multimodal outputcited by 15
  6. 2024A Foundation Model for Brain Lesion Segmentation with Mixture of Modality Expertscited by 14
  7. 2024MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidancecited by 13
  8. 2025SAM2 for Image and Video Segmentation: A Comprehensive Surveycited by 13
  9. 2025NOVA: A Benchmark for Anomaly Localization and Clinical Reasoning in Brain MRIcited by 13
  10. 2023Panoptica -- instance-wise evaluation of 3D semantic and instance segmentation mapscited by 12
  11. 2022MAPPING: Model Average with Post-processing for Stroke Lesion Segmentationcited by 9
  12. 2024PEPSI: Pathology-Enhanced Pulse-Sequence-Invariant Representations for Brain MRIcited by 8
  13. 2024A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challengecited by 8
  14. 2025In the Picture: Medical Imaging Datasets, Artifacts, and their Living Reviewcited by 8
  15. 2025DyCON: Dynamic Uncertainty-aware Consistency and Contrastive Learning for Semi-supervised Medical Image Segmentationcited by 8
  16. 2025MedSG-Bench: A Benchmark for Medical Image Sequences Groundingcited by 8
  17. 2025A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learningcited by 8
  18. 2025OmniBrainBench: A Comprehensive Multimodal Benchmark for Brain Imaging Analysis Across Multi-stage Clinical Taskscited by 8
  19. 2025CenterMamba-SAM: Center-Prioritized Scanning and Temporal Prototypes for Brain Lesion Segmentationcited by 8
  20. 2024ISLES'24 -- A Real-World Longitudinal Multimodal Stroke Datasetcited by 6
  21. 2024VoxelPrompt: A Vision Agent for End-to-End Medical Image Analysiscited by 6
  22. 2023Large-kernel Attention for Efficient and Robust Brain Lesion Segmentationcited by 5
  23. 2024Transformers-based architectures for stroke segmentation: A reviewcited by 5
  24. 2023APIS: A paired CT-MRI dataset for ischemic stroke segmentation challengecited by 4
  25. 2023Unsupervised Anomaly Detection using Aggregated Normative Diffusioncited by 4
  26. 2024Synthetic Data for Robust Stroke Segmentationcited by 4
  27. 2024ISLES'24: Final Infarct Prediction with Multimodal Imaging and Clinical Data. Where Do We Stand?cited by 4
  28. 2024Autoregressive Sequence Modeling for 3D Medical Image Representationcited by 4
  29. 2025Unraveling Normal Anatomy via Fluid-Driven Anomaly Randomizationcited by 4
  30. 2024Deep models for stroke segmentation: do complex architectures always perform better?cited by 3
  31. 2025Deep Learning-Driven Segmentation of Ischemic Stroke Lesions Using Multi-Channel MRIcited by 3
  32. 2025Flatten Wisely: How Patch Order Shapes Mamba-Powered Vision for MRI Segmentationcited by 3
  33. 2025Medverse: A Universal Model for Full-Resolution 3D Medical Image Segmentation, Transformation and Enhancementcited by 3
  34. 2025ReX-MLE: The Autonomous Agent Benchmark for Medical Imaging Challengescited by 3
  35. 2022Automated ischemic stroke lesion segmentation from 3D MRIcited by 2
  36. 2024Rethinking Annotator Simulation: Realistic Evaluation of Whole-Body PET Lesion Interactive Segmentation Methodscited by 2
  37. 2024MedSAGa: Few-shot Memory Efficient Medical Image Segmentation using Gradient Low-Rank Projection in SAMcited by 2
  38. 2024Improving Factuality of 3D Brain MRI Report Generation with Paired Image-domain Retrieval and Text-domain Augmentationcited by 2

50 most cited of 106 All 106 (JSON)