ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset
Moritz Roman Hernández Petzsche, Ezequiel de la Rosa, Uta Hanning, Roland Wiest, Waldo Valenzuela, Mauricio Reyes, Maria Inês Meyer, Sook‐Lei Liew, Florian Kofler, Ivan Ezhov, David Robben, A. Hutton, Tassilo Friedrich, Teresa Zarth, Johannes Bürkle, The Anh Baran, Bjoern Menze, Gabriel Broocks, Lukas Meyer, Claus Zimmer, Tobias Boeckh‐Behrens, Maria Berndt, Benno Ikenberg, Benedikt Wiestler, Jan S. Kirschke
Abstract
Magnetic resonance imaging (MRI) is an important imaging modality in stroke. Computer based automated medical image processing is increasingly finding its way into clinical routine. The Ischemic Stroke Lesion Segmentation (ISLES) challenge is a continuous effort to develop and identify 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 ( https://doi.org/10.5281/zenodo.7153326 ). 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 is 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 ( https://www.isles-challenge.org/ ) with the goal of finding algorithmic methods to enable the development and benchmarking of automatic, robust and accurate segmentation methods for ischemic stroke.