ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI

Winzeck S, Hakim A, Mckinley R, Pinto JAADSR, Alves V, Silva C, Pisov M, Krivov E, Belyaev M, Monteiro M, Oliveira A, Choi Y, Palk MC, Kwon Y, Lee H, Kim BJ, Won JH, Islam M, Ren H, Robben D, Suetens P, Gong E, Niu Y, Xu J, Pauly JM, Lucas C, Heinrich MP, Rivera LC, Castillo LS, Daza LA, Beers AL, Arbelaers P, Maier O, Chang K, Brown JM, Kapalthy-Cramer J, Zaharchuk G, Wiest R, Reyes M (2018)

Publication Type: Journal article

Publication year: 2018

Journal

Frontiers in Neurology Frontiers Research Foundation / Frontiers Media

Book Volume: 9

Article Number: 679

Journal Issue: SEP

DOI: 10.3389/fneur.2018.00679

Abstract

Performance of models highly depend not only on the used algorithm but also the data set it was applied to. This makes the comparison of newly developed tools to previously published approaches difficult. Either researchers need to implement others' algorithms first, to establish an adequate benchmark on their data, or a direct comparison of new and old techniques is infeasible. The Ischemic Stroke Lesion Segmentation (ISLES) challenge, which has ran now consecutively for 3 years, aims to address this problem of comparability. ISLES 2016 and 2017 focused on lesion outcome prediction after ischemic stroke: By providing a uniformly pre-processed data set, researchers from all over the world could apply their algorithm directly. A total of nine teams participated in ISLES 2015, and 15 teams participated in ISLES 2016. Their performance was evaluated in a fair and transparent way to identify the state-of-the-art among all submissions. Top ranked teams almost always employed deep learning tools, which were predominately convolutional neural networks (CNNs). Despite the great efforts, lesion outcome prediction persists challenging. The annotated data set remains publicly available and new approaches can be compared directly via the online evaluation system, serving as a continuing benchmark (www.isles-challenge.org).

Authors with CRIS profile

Luis Rivera Monroy

Involved external institutions

Universidad de los Andes

Chile (CL) Harvard University

United States (USA) (US) Universidade do Minho

Portugal (PT) Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa (INESC-ID)

Portugal (PT) Seoul National University (SNU) / 서울대학교

Korea, Republic of (KR) Universität Bern

Switzerland (CH) Stanford University

United States (USA) (US) University of Cambridge

United Kingdom (GB) National University of Singapore (NUS)

Singapore (SG) Moscow Institute of Physics and Technology (MIPT, PhysTech) / Московский Физико-Технический институт (Физтех)

Russian Federation (RU) Universität zu Lübeck

Germany (DE) Katholieke Universiteit Leuven (KUL) / Catholic University of Leuven

Belgium (BE) Institute for Information Transmission Problems of Russian Academy of Sciences (Kharkevich Institute, IITP RAS) / Институт проблем передачи информации имени А. А. Харкевича (ИППИ) РАН

Russian Federation (RU) Tsinghua University

China (CN)

How to cite

APA:

Winzeck, S., Hakim, A., Mckinley, R., Pinto, J.A.A.D.S.R., Alves, V., Silva, C.,... Reyes, M. (2018). ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI. Frontiers in Neurology, 9(SEP). https://doi.org/10.3389/fneur.2018.00679

MLA:

Winzeck, Stefan, et al. "ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI." Frontiers in Neurology 9.SEP (2018).

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