ISLES'24 -- A Real-World Longitudinal Multimodal Stroke Dataset

• URL: http://arxiv.org/abs/2408.11142v2
• Date: Sun, 06 Jul 2025 08:42:40 GMT
• Title: ISLES'24 -- A Real-World Longitudinal Multimodal Stroke Dataset
• Authors: Evamaria Olga Riedel, Ezequiel de la Rosa, The Anh Baran, Moritz Hernandez Petzsche, Hakim Baazaoui, Kaiyuan Yang, Fabio Antonio Musio, Houjing Huang, David Robben, Joaquin Oscar Seia, Roland Wiest, Mauricio Reyes, Ruisheng Su, Claus Zimmer, Tobias Boeckh-Behrens, Maria Berndt, Bjoern Menze, Daniel Rueckert, Benedikt Wiestler, Susanne Wegener, Jan Stefan Kirschke,
• Abstract summary: Stroke remains a leading cause of global morbidity and mortality, imposing a heavy socioeconomic burden.<n> Developing machine learning algorithms that can create accurate models of brain function from stroke images requires large, diverse, and well annotated public datasets.<n>This multicenter dataset consists of 245 cases and is a solid basis for developing powerful machine-learning algorithms to facilitate clinical decision-making.
• Score: 9.501255615225428
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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