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

Maier, Oskar; Menze, Bjoern H; von der Gablentz, Janina; Häni, Levin; Heinrich, Mattias P; Liebrand, Matthias; Winzeck, Stefan; Basit, Abdul; Bentley, Paul; Chen, Liang; Christiaens, Daan; Dutil, Francis; Egger, Karl; Feng, Chaolu; Glocker, Ben; Götz, Michael; Haeck, Tom; Halme, Hanna-Leena; Havaei, Mohammad; Iftekharuddin, Khan M; ... (2016). ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Medical image analysis, 35, pp. 250-269. Elsevier 10.1016/j.media.2016.07.009

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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 (www.isles-challenge.org).

Item Type:

Journal Article (Original Article)

Division/Institute:

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

UniBE Contributor:

Häni, Levin; McKinley, Richard; Wiest, Roland and Reyes, Mauricio

Subjects:

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

ISSN:

1361-8415

Publisher:

Elsevier

Language:

English

Submitter:

Martin Zbinden

Date Deposited:

13 Sep 2016 13:55

Last Modified:

20 Mar 2017 16:50

Publisher DOI:

10.1016/j.media.2016.07.009

PubMed ID:

27475911

Uncontrolled Keywords:

Benchmark; Challenge; Comparison; Ischemic stroke; MRI; Segmentation

BORIS DOI:

10.7892/boris.87516

URI:

https://boris.unibe.ch/id/eprint/87516

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