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Computational Intelligence and Neuroscience
Volume 2015 (2015), Article ID 813696, 16 pages
http://dx.doi.org/10.1155/2015/813696
Research Article

MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans

1Image Sciences Institute, University Medical Center Utrecht, 3584 CX Utrecht, Netherlands
2Philips Healthcare, 5680 DA Best, Netherlands
3Faculty of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, Netherlands
4Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, 3584 CX Utrecht, Netherlands
5Department of Radiology, University Medical Center Utrecht, 3584 CX Utrecht, Netherlands
6BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
7Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus MC, 3015 CN Rotterdam, Netherlands
8Department of Computer Science, University of Copenhagen, 2100 Copenhagen, Denmark
9Image Analysis and Communications Laboratory, Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
10Department of Electronics and Communication Engineering, The LNM Institute of Information Technology, Jaipur 302031, India
11Imaging Laboratories, Robarts Research Institute, London, ON, Canada N6A 5B7
12Department of Medical Biophysics, Western University, London, ON, Canada N6A 3K7
13Signals and Systems, Chalmers University of Technology, 41296 Gothenburg, Sweden
14Applied Physics Laboratory, Johns Hopkins University, Laurel, MD 20723, USA
15Department of Electronics, University of Minho, 4800-058 Guimarães, Portugal
16Department of Computing, Imperial College London, London SW7 2AZ, UK
17Computer Science and Engineering Department, State University of New York at Buffalo, Buffalo, NY 14260-2500, USA
18Center for Medical Imaging Science and Visualization, Linköping University, 58185 Linköping, Sweden
19Department of Radiology and Department of Medical and Health Sciences, Linköping University, 58185 Linköping, Sweden

Received 10 July 2015; Accepted 19 August 2015

Academic Editor: Jussi Tohka

Copyright © 2015 Adriënne M. Mendrik et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.