ISLES’24: Final stroke infarct prediction with multimodal imaging and clinical data. Where do we stand?
Year: 2026
Type: article
Abstract: Accurate estimation of brain infarction (i.e., irreversibly damaged tissue) is critical for guiding treatment decisions in acute ischemic stroke. Reliable infarct prediction informs key clinical interventions, including the need for patient transfer to comprehensive stroke centers, the potential benefit of additional reperfusion attempts during mechanical thrombectomy, decisions regarding secondary neuroprotective treatments, and, ultimately, prognosis of clinical outcomes. This work introduces the Ischemic Stroke Lesion Segmentation (ISLES) 2024 challenge, which focuses on the prediction of final infarct volumes from pre-interventional acute stroke imaging and clinical data. ISLES'24 provides a comprehensive, multimodal setting where participants can leverage all clinically and practically available data - including full acute CT imaging, sub-acute follow-up MRI, and structured clinical information - across a train set of N=150 cases. On the hidden test set (N=98), the top-performing model, a multimodal nnU-Net-based architecture, achieved a Dice score of 0.285 ± 0.213 and an absolute volume difference of 21.2 ± 37.2 mL, underlining the significant challenges posed by this task and the need for further advances in multimodal learning. This overview of the ISLES'24 challenge offers two primary contributions: first, it provides a standardized, clinically realistic benchmark for post-treatment infarct prediction, built on a comprehensive, open, and multimodal longitudinal stroke imaging dataset; second, it provides insights into the strengths and limitations of current algorithms and delineates key research directions to guide the development of next-generation infarct prediction models.
Source: Medical Image Analysis
Authors: Ezequiel de la Rosa, Ruisheng Su, Mauricio Reyes, Evamaria O. Riedel, Hakim Baazaoui, The Anh Baran, Roland Wiest, Florian Kofler, Kaiyuan Yang, Murong Xu, Julian Deseoe, Lisa Herzog, David Robben, Mahsa Mojtahedi, Laura van Poppel, Lucas de Vries, Anthony Winder, Kimberly Amador, Nils D. Forkert, Gyeongyeon Hwang, Jiwoo Song, Dohyun Kim, Eneko Uruñuela, Annabella Bregazzi, Matthias Wilms, Hyun Yang, Jin Tae Kwak, Sumin Jung, Luan Matheus Trindade Dalmazo, Kumaradevan Punithakumar, Moona Mazher, Abdul Qayyum, Steven Niederer, Jacob Idoko, Mariana Bento, Gouri Ginde, Tianyi Ren, Juampablo Heras Rivera, Mehmet Kurt, Cansu Yalcin, Adrià Casamitjana, Arnau Oliver, Xavier Lladó, Carole Frindel, Susanne Wegener, Jan S. Kirschke, Benedikt Wiestler, Bjoern Menze
Institutions: University of Zurich, Eindhoven University of Technology, University of Bern, University Hospital of Bern, TUM Klinikum, Technical University of Munich, University Hospital Zurich, Helmholtz Munich, Icometrix (Belgium), Amsterdam UMC Location University of Amsterdam, University of Calgary, Hotchkiss Brain Institute, Alberta Children's Hospital Research Institute, Korea University, University of Alberta, University College London, Imperial College London, University of Washington, Universitat de Girona, Lyon 1 Université, Centre National de la Recherche Scientifique, Inserm, Institut Universitaire de France, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé, Institut National des Sciences Appliquées de Lyon
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,170
Funders: National Science Foundation, U.S. Department of Energy, Heart and Stroke Foundation of Canada, Universitat de Girona, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Institució Catalana de Recerca i Estudis Avançats, Schweizerische Herzstiftung, Helmut Horten Stiftung
OpenAlex ID: W4403012039