ISLES’24: Final stroke infarct prediction with multimodal imaging and clinical data. Where do we stand?

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.

The authors' abstract, as published at the source. Medical Image Analysis, 2026 · DOI ↗