ISLES 2015-A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI
Artikel i vetenskaplig tidskrift, 2017

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).

MRI

Comparison

Challenge

Segmentation

Ischemic stroke

Benchmark

Författare

O. Maier

Universitat zu Lubeck

Bjoern H. Menze

Technische Universität München

J. von der Gablentz

Universitat zu Lubeck

L. Hani

Universität Bern

Mattias Heinrich

Universitat zu Lubeck

M. Liebrand

Universitat zu Lubeck

S. Winzeck

Technische Universität München

A. Basit

Pakistan Institute of Nuclear Science and Technology

P. Bentley

Imperial College London

L. Chen

Imperial College London

D. Christiaens

KU Leuven– University Hospital Leuven

KU Leuven

F. Dutil

Université de Sherbrooke

K. Egger

Universitats Klinikum Freiburg und Medizinische Fakultat

C. L. Feng

Northeastern University China

B. Glocker

Imperial College London

M. Gotz

German Cancer Research Center

T. Haeck

KU Leuven– University Hospital Leuven

KU Leuven

H. L. Halme

Aalto-Yliopisto

Helsingin Yliopisto

M. Havaei

Université de Sherbrooke

K. M. Iftekharuddin

Old Dominion University

P. M. Jodoin

Université de Sherbrooke

K. Kamnitsas

Imperial College London

E. Kellner

Universitats Klinikum Freiburg und Medizinische Fakultat

A. Korvenoja

Helsingin Yliopisto

H. Larochelle

Université de Sherbrooke

C. Ledig

Imperial College London

J. H. Lee

National Taiwan University of Science and Technology

F. Maes

KU Leuven– University Hospital Leuven

KU Leuven

Mahmood Qaiser

Signaler och system, Signalbehandling och medicinsk teknik, Biomedicinsk elektromagnetik

Andra publikationer Forskning

K. H. Maier-Hein

German Cancer Research Center

R. McKinley

UniversitatsSpital Bern

J. Muschelli

Johns Hopkins Bloomberg School of Public Health

C. Pal

Ecole Polytechnique de Montreal

L. M. Pei

Old Dominion University

J. R. Rangarajan

KU Leuven– University Hospital Leuven

KU Leuven

S. M. S. Reza

Old Dominion University

D. Robben

KU Leuven

KU Leuven– University Hospital Leuven

D. Rueckert

Imperial College London

E. Salli

Helsingin Yliopisto

P. Suetens

KU Leuven

KU Leuven– University Hospital Leuven

C. W. Wang

National Taiwan University of Science and Technology

M. Wilms

Universitat zu Lubeck

J. S. Kirschke

Klinikum rechts der Isar

U. M. Kramer

Universitat zu Lubeck

T. F. Munte

Universitat zu Lubeck

P. Schramme

University Medical Center Lübeck

R. Wiest

UniversitatsSpital Bern

H. Handels

Universitat zu Lubeck

M. Reyes

Universität Bern

Medical Image Analysis

1361-8415 (ISSN)

Vol. 35 250-269

Ämneskategorier

Datorseende och robotik (autonoma system)

DOI

10.1016/j.media.2016.07.009

Publikationsdata kopplat till DOI

PubMed

27475911

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