MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans
Artikel i vetenskaplig tidskrift, 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.
Författare
A. M. Mendrik
Image Sciences Institute
K. L. Vincken
Image Sciences Institute
H. J. Kuijf
Image Sciences Institute
M. Breeuwer
Technische Universiteit Eindhoven
Philips Healthcare 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 Medical Center
Kobenhavns Universitet
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
Robarts Research Institute
F. van der Lijn
Erasmus University Medical Center
Mahmood Qaiser
Chalmers, Signaler och system, Signalbehandling och medicinsk teknik
R. Mukherjee
The Johns Hopkins University Applied Physics Laboratory
A. van Opbroek
Erasmus University Medical Center
S. Paneri
LNM Institute of Information Technology (Deemed University), Jaipur
S. Pereira
Universidade do Minho
Mikael Persson
Chalmers, Signaler och system, Signalbehandling och medicinsk teknik
M. Rajchl
Imperial College London
Robarts Research Institute
D. Sarikaya
University at Buffalo, State University of New York
O. Smedby
Linkopings universitet
C. A. Silva
Universidade do Minho
H. A. Vrooman
Erasmus University Medical Center
S. Vyas
The Johns Hopkins University Applied Physics Laboratory
C. L. Wang
Linkopings universitet
L. Zhao
University at Buffalo, State University of New York
G. J. Biessels
University Medical Center Utrecht
M. A. Viergever
Image Sciences Institute
Computational Intelligence and Neuroscience
1687-5265 (ISSN) 1687-5273 (eISSN)
813696-Ämneskategorier (SSIF 2011)
Annan medicinteknik
Styrkeområden
Livsvetenskaper och teknik (2010-2018)
DOI
10.1155/2015/813696