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).
Challenge
Benchmark
Segmentation
MRI
Ischemic stroke
Comparison
Författare
O. Maier
Universitat zu Lubeck
Bjoern H. Menze
Technische Universitat Munchen
J. von der Gablentz
Universitat zu Lubeck
L. Hani
Universitat Bern
Mattias Heinrich
Universitat zu Lubeck
M. Liebrand
Universitat zu Lubeck
S. Winzeck
Technische Universitat Munchen
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
Universite 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
KU Leuven– University Hospital Leuven
H. L. Halme
Helsingin Yliopisto
Aalto University
M. Havaei
Universite de Sherbrooke
K. M. Iftekharuddin
Old Dominion University
P. M. Jodoin
Universite de Sherbrooke
K. Kamnitsas
Imperial College London
E. Kellner
Universitats Klinikum Freiburg und Medizinische Fakultat
A. Korvenoja
Helsingin Yliopisto
H. Larochelle
Universite de Sherbrooke
C. Ledig
Imperial College London
J. H. Lee
National Taiwan University of Science and Technology
F. Maes
KU Leuven
KU Leuven– University Hospital Leuven
Mahmood Qaiser
Chalmers, Signaler och system, Signalbehandling och medicinsk teknik
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
KU Leuven– University Hospital 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– University Hospital Leuven
KU 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
Universitat Bern
Medical Image Analysis
1361-8415 (ISSN)
Vol. 35 250-269Ämneskategorier (SSIF 2011)
Datorseende och robotik (autonoma system)
DOI
10.1016/j.media.2016.07.009
PubMed
27475911