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

Author:

Mendrik Adriënne M.1,Vincken Koen L.1,Kuijf Hugo J.1,Breeuwer Marcel23,Bouvy Willem H.4,de Bresser Jeroen5,Alansary Amir6,de Bruijne Marleen78,Carass Aaron9,El-Baz Ayman6,Jog Amod9,Katyal Ranveer10,Khan Ali R.1112,van der Lijn Fedde7,Mahmood Qaiser13,Mukherjee Ryan14,van Opbroek Annegreet7,Paneri Sahil10,Pereira Sérgio15,Persson Mikael13ORCID,Rajchl Martin1116,Sarikaya Duygu17,Smedby Örjan1819,Silva Carlos A.15,Vrooman Henri A.7,Vyas Saurabh14,Wang Chunliang1819,Zhao Liang17,Biessels Geert Jan4,Viergever Max A.1

Affiliation:

1. Image Sciences Institute, University Medical Center Utrecht, 3584 CX Utrecht, Netherlands

2. Philips Healthcare, 5680 DA Best, Netherlands

3. Faculty of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, Netherlands

4. Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, 3584 CX Utrecht, Netherlands

5. Department of Radiology, University Medical Center Utrecht, 3584 CX Utrecht, Netherlands

6. BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA

7. Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus MC, 3015 CN Rotterdam, Netherlands

8. Department of Computer Science, University of Copenhagen, 2100 Copenhagen, Denmark

9. Image Analysis and Communications Laboratory, Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA

10. Department of Electronics and Communication Engineering, The LNM Institute of Information Technology, Jaipur 302031, India

11. Imaging Laboratories, Robarts Research Institute, London, ON, Canada N6A 5B7

12. Department of Medical Biophysics, Western University, London, ON, Canada N6A 3K7

13. Signals and Systems, Chalmers University of Technology, 41296 Gothenburg, Sweden

14. Applied Physics Laboratory, Johns Hopkins University, Laurel, MD 20723, USA

15. Department of Electronics, University of Minho, 4800-058 Guimarães, Portugal

16. Department of Computing, Imperial College London, London SW7 2AZ, UK

17. Computer Science and Engineering Department, State University of New York at Buffalo, Buffalo, NY 14260-2500, USA

18. Center for Medical Imaging Science and Visualization, Linköping University, 58185 Linköping, Sweden

19. Department of Radiology and Department of Medical and Health Sciences, Linköping University, 58185 Linköping, Sweden

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.

Funder

ZonMw

Publisher

Hindawi Limited

Subject

General Mathematics,General Medicine,General Neuroscience,General Computer Science

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