  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.
