ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

Oskar Maier*, Bjoern H. Menze, Janina von der Gablentz, Levin Häni, Mattias P. Heinrich, Matthias Liebrand, Stefan Winzeck, Abdul Basit, Paul Bentley, Liang Chen, Daan Christiaens, Francis Dutil, Karl Egger, Chaolu Feng, Ben Glocker, Michael Götz, Tom Haeck, Hanna Leena Halme, Mohammad Havaei, Khan M. IftekharuddinPierre Marc Jodoin, Konstantinos Kamnitsas, Elias Kellner, Antti Korvenoja, Hugo Larochelle, Christian Ledig, Jia Hong Lee, Frederik Maes, Qaiser Mahmood, Klaus H. Maier-Hein, Richard McKinley, John Muschelli, Chris Pal, Linmin Pei, Janaki Raman Rangarajan, Syed M.S. Reza, David Robben, Daniel Rueckert, Eero Salli, Paul Suetens, Ching Wei Wang, Matthias Wilms, Jan S. Kirschke, Ulrike M. Krämer, Thomas F. Münte, Peter Schramm, Roland Wiest, Heinz Handels, Mauricio Reyes

*Corresponding author for this work
77 Citations (Scopus)


Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (

Original languageEnglish
JournalMedical Image Analysis
Pages (from-to)250-269
Number of pages20
Publication statusPublished - 01.01.2017

Research Areas and Centers

  • Academic Focus: Center for Brain, Behavior and Metabolism (CBBM)


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