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Neuroimage. 2017 May 15;152:312-329. doi: 10.1016/j.neuroimage.2017.03.010. Epub 2017 Mar 7.

Spinal cord grey matter segmentation challenge.

Author information

1
Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, Malet Place Engineering Building, London WC1E 6BT, UK; NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, Russell Square, London WC1B 5EH, UK. Electronic address: f.carrasco@ucl.ac.uk.
2
Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK.
3
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada V6T 1Z4.
4
Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
5
Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, Malet Place Engineering Building, London WC1E 6BT, UK; Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UK.
6
Department of Electrical Engineering, Computer Science, Biomedical Engineering, Radiology and Radiological Sciences, Institute of Image Science at Vanderbilt University, Nashville, TN, USA.
7
Spinal Cord Injury Center Balgrist, University Hospital Zurich, University of Zurich, Switzerland.
8
NeuroPoly Lab, Polytechnique Montreal, Montreal, QC, Canada.
9
NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, Russell Square, London WC1B 5EH, UK; Brain MRI 3T Centre, C. Mondino National Neurological Institute, Pavia, Italy; Department of Brain and Behavioural Sciences, University of Pavia, Italy.
10
NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, Russell Square, London WC1B 5EH, UK.
11
Department of Medicine, University of British Columbia, Vancouver, BC, Canada V6T 2B5.
12
Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA.
13
Eindhoven University of Technology, Netherlands.
14
Department of Radiology and Radiological Sciences, Biomedical Engineering, Ophthalmology, Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA.
15
Department of Radiology, European Institute of Oncology, University of Modena and Reggio Emilia, 41121, Modena, MO, Italy.
16
Department of Radiology, UBC MS/MRI Research Group, University of British Columbia, Vancouver, BC, Canada V6T 2B5.
17
NeuroPoly Lab, Polytechnique Montreal, Montreal, QC, Canada; Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada. Electronic address: jcohen@polymtl.ca.

Abstract

An important image processing step in spinal cord magnetic resonance imaging is the ability to reliably and accurately segment grey and white matter for tissue specific analysis. There are several semi- or fully-automated segmentation methods for cervical cord cross-sectional area measurement with an excellent performance close or equal to the manual segmentation. However, grey matter segmentation is still challenging due to small cross-sectional size and shape, and active research is being conducted by several groups around the world in this field. Therefore a grey matter spinal cord segmentation challenge was organised to test different capabilities of various methods using the same multi-centre and multi-vendor dataset acquired with distinct 3D gradient-echo sequences. This challenge aimed to characterize the state-of-the-art in the field as well as identifying new opportunities for future improvements. Six different spinal cord grey matter segmentation methods developed independently by various research groups across the world and their performance were compared to manual segmentation outcomes, the present gold-standard. All algorithms provided good overall results for detecting the grey matter butterfly, albeit with variable performance in certain quality-of-segmentation metrics. The data have been made publicly available and the challenge web site remains open to new submissions. No modifications were introduced to any of the presented methods as a result of this challenge for the purposes of this publication.

KEYWORDS:

Challenge; Evaluation metrics; Grey matter; MRI; Segmentation; Spinal cord

PMID:
28286318
PMCID:
PMC5440179
DOI:
10.1016/j.neuroimage.2017.03.010
[Indexed for MEDLINE]
Free PMC Article

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