ISLES'24 -- A Real-World Longitudinal Multimodal Stroke Dataset
Abstract
Stroke remains a leading cause of global morbidity and mortality, imposing a heavy socioeconomic burden. Advances in endovascular reperfusion therapy and CT and MR imaging for treatment guidance have significantly improved patient outcomes. Developing machine learning algorithms that can create accurate models of brain function from stroke images for tasks like lesion identification and tissue survival prediction requires large, diverse, and well annotated public datasets. While several high-quality image datasets in stroke exist, they include only single time point data. Data over different time points are essential to accurately identify lesions and predict prognosis. Here, we provide comprehensive longitudinal stroke data, including (sub-)acute CT imaging with angiography and perfusion, follow-up MRI after 2-9 days, and acute and longitudinal clinical data up to a three-month outcome. The dataset also includes vessel occlusion masks from acute CT angiography and delineated infarction masks in follow-up MRI. This multicenter dataset consists of 245 cases and is a solid basis for developing powerful machine-learning algorithms to facilitate clinical decision-making.
Cite
@article{arxiv.2408.11142,
title = {ISLES'24 -- A Real-World Longitudinal Multimodal Stroke Dataset},
author = {Evamaria Olga Riedel and Ezequiel de la Rosa and The Anh Baran and Moritz Hernandez Petzsche and Hakim Baazaoui and Kaiyuan Yang and Fabio Antonio Musio and Houjing Huang and David Robben and Joaquin Oscar Seia and Roland Wiest and Mauricio Reyes and Ruisheng Su and Claus Zimmer and Tobias Boeckh-Behrens and Maria Berndt and Bjoern Menze and Daniel Rueckert and Benedikt Wiestler and Susanne Wegener and Jan Stefan Kirschke},
journal= {arXiv preprint arXiv:2408.11142},
year = {2025}
}