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Med Image Anal. 2017 Jan;35:250-269. doi: 10.1016/j.media.2016.07.009. Epub 2016 Jul 21.

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

Author information

1
Institut for Medical Informatics, University of Lübeck, Lübeck, Germany.
2
Graduate School for Computing in Medicine and Live Science, University of Lübeck, Germany.
3
Institute for Advanced Study and Department of Computer Science, Technische Universität München, Munich, Germany.
4
Department of Neurology, University of Lübeck, Germany.
5
Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland.
6
Pakistan Institute of Nuclear Science and Technology, Islamabad, Pakistan.
7
Division of Brain Sciences, Department of Medicine, Imperial College London, UK.
8
Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK.
9
ESAT/PSI, Department of Electrical Engineering, KU Leuven, Belgium.
10
Medical Imaging Research Center, UZ Leuven, Belgium.
11
Université de Sherbrooke, Sherbrooke, Qc, Canada.
12
Department of Neuroradiology, University Medical Center Freiburg, Germany.
13
College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China.
14
Junior Group Medical Image Computing, German Cancer Research Center, Heidelberg, Germany.
15
HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
16
Department of Neuroscience and Biomedical Engineering NBE, Aalto University School of Science, Aalto, Finland.
17
Vision Lab, Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA, USA.
18
Department of Radiology, Medical Physics, University Medical Center Freiburg, Germany.
19
Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei City, Taiwan.
20
Signals and Systems, Chalmers University of Technology, Gothenburg, Sweden.
21
Department of Diagnostic and Interventional Neuroradiology, Inselspital Bern, Switzerland.
22
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
23
Ecole Polytechnique de Montréal, Canada.
24
Department of Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.
25
Institute of Psychology II, University of Lübeck, Germany.
26
Institute of Neuroradiology, University Medical Center Lübeck.
#
Contributed equally

Abstract

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).

KEYWORDS:

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

PMID:
27475911
PMCID:
PMC5099118
DOI:
10.1016/j.media.2016.07.009
[Indexed for MEDLINE]
Free PMC Article

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