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Magnetic resonance imaging (MRI) is a central modality for stroke imaging. Itis used upon patient admission to make treatment decisions such as selectingpatients for intravenous thrombolysis or endovascular therapy. MRI is laterused in the duration of hospital stay to predict outcome by visualizing infarctcore size and location. Furthermore, it may be used to characterize strokeetiology, e.g. differentiation between (cardio)-embolic and non-embolic stroke.Computer based automated medical image processing is increasingly finding itsway into clinical routine. Previous iterations of the Ischemic Stroke LesionSegmentation (ISLES) challenge have aided in the generation of identifyingbenchmark methods for acute and sub-acute ischemic stroke lesion segmentation.Here we introduce an expert-annotated, multicenter MRI dataset for segmentationof acute to subacute stroke lesions. This dataset comprises 400 multi-vendorMRI 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. Alltraining data will be made publicly available. The test dataset will be usedfor model validation only and will not be released to the public. This datasetserves as the foundation of the ISLES 2022 challenge with the goal of findingalgorithmic methods to enable the development and benchmarking of robust andaccurate segmentation algorithms for ischemic stroke.

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