MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans
Journal article, 2015
Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi) automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65-80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.
Author
A. M. Mendrik
Image Sciences Institute
K. L. Vincken
Image Sciences Institute
H. J. Kuijf
Image Sciences Institute
M. Breeuwer
Eindhoven University of Technology
Philips Medical Systems Nederland
W. H. Bouvy
University Medical Center Utrecht
J. de Bresser
University Medical Center Utrecht
A. Alansary
University of Louisville
M. de Bruijne
Erasmus University Rotterdam
University of Copenhagen
A. Carass
Johns Hopkins University
A. El-Baz
University of Louisville
A. Jog
Johns Hopkins University
R. Katyal
LNM Institute of Information Technology (Deemed University), Jaipur
A. R. Khan
Western University
F. van der Lijn
Erasmus University Rotterdam
Mahmood Qaiser
Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering
R. Mukherjee
The Johns Hopkins University Applied Physics Laboratory
A. van Opbroek
Erasmus University Rotterdam
S. Paneri
LNM Institute of Information Technology (Deemed University), Jaipur
S. Pereira
University of Minho
Mikael Persson
Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering
M. Rajchl
Western University
Imperial College London
D. Sarikaya
SUNY Buffalo
O. Smedby
Linköping University
C. A. Silva
University of Minho
H. A. Vrooman
Erasmus University Rotterdam
S. Vyas
The Johns Hopkins University Applied Physics Laboratory
C. L. Wang
Linköping University
L. Zhao
SUNY Buffalo
G. J. Biessels
University Medical Center Utrecht
M. A. Viergever
Image Sciences Institute
Computational Intelligence and Neuroscience
1687-5265 (ISSN) 1687-5273 (eISSN)
Vol. 2015 813696Subject Categories
Other Medical Engineering
Areas of Advance
Life Science Engineering (2010-2018)
DOI
10.1155/2015/813696