Authors
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 Iftekharuddin, Pierre-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 MS 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
Publication date
2017/1/1
Journal
Medical image analysis
Volume
35
Pages
250-269
Publisher
Elsevier
Description
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 …
Scholar articles
O Maier, BH Menze, J Von der Gablentz, L Häni… - Medical image analysis, 2017