ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset
This dataset addresses the need for standardized benchmarks in stroke lesion segmentation, aiding researchers and clinicians in improving automated medical image processing for stroke diagnosis and treatment, though it is incremental as it builds on previous ISLES challenges.
The paper introduces a new expert-annotated, multicenter MRI dataset of 400 cases for segmenting acute to subacute stroke lesions, split into 250 training and 150 test cases, to support the ISLES 2022 challenge for developing and benchmarking robust segmentation algorithms.
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.