ISLES 2016 and 2017-Benchmarking Ischemic Stroke Lesion Outcome Prediction Based on Multispectral MRI.

Winzeck, Stefan; Hakim, Arsany; McKinley, Richard; Pinto, José A A D S R; Alves, Victor; Silva, Carlos; Pisov, Maxim; Krivov, Egor; Belyaev, Mikhail; Monteiro, Miguel; Oliveira, Arlindo; Choi, Youngwon; Paik, Myunghee Cho; Kwon, Yongchan; Lee, Hanbyul; Kim, Beom Joon; Won, Joong-Ho; Islam, Mobarakol; Ren, Hongliang; Robben, David; ... (2018). ISLES 2016 and 2017-Benchmarking Ischemic Stroke Lesion Outcome Prediction Based on Multispectral MRI. Frontiers in neurology, 9(679), p. 679. Frontiers Media S.A. 10.3389/fneur.2018.00679

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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).

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology
04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB

UniBE Contributor:

Hakim, Arsany; McKinley, Richard; Wiest, Roland and Reyes, Mauricio

Subjects:

600 Technology > 610 Medicine & health
500 Science > 570 Life sciences; biology

ISSN:

1664-2295

Publisher:

Frontiers Media S.A.

Language:

English

Submitter:

Martin Zbinden

Date Deposited:

08 Oct 2018 07:50

Last Modified:

14 Oct 2018 02:30

Publisher DOI:

10.3389/fneur.2018.00679

PubMed ID:

30271370

Uncontrolled Keywords:

MRI benchmarking datasets deep learning machine learning prediction models stroke stroke outcome

BORIS DOI:

10.7892/boris.120350

URI:

https://boris.unibe.ch/id/eprint/120350

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