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Sci Rep. 2016 Oct 27;6:36151. doi: 10.1038/srep36151.

Fully automated grey and white matter spinal cord segmentation.

Author information

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, 1st Floor, Russell Square House, 10-12 Russell Square, London, WC1B 5EH, UK.
Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK.
Brain MRI 3T Center, C. Mondino National Neurological Institute, Pavia, Italy.


Axonal loss in the spinal cord is one of the main contributing factors to irreversible clinical disability in multiple sclerosis (MS). In vivo axonal loss can be assessed indirectly by estimating a reduction in the cervical cross-sectional area (CSA) of the spinal cord over time, which is indicative of spinal cord atrophy, and such a measure may be obtained by means of image segmentation using magnetic resonance imaging (MRI). In this work, we propose a new fully automated spinal cord segmentation technique that incorporates two different multi-atlas segmentation propagation and fusion techniques: The Optimized PatchMatch Label fusion (OPAL) algorithm for localising and approximately segmenting the spinal cord, and the Similarity and Truth Estimation for Propagated Segmentations (STEPS) algorithm for segmenting white and grey matter simultaneously. In a retrospective analysis of MRI data, the proposed method facilitated CSA measurements with accuracy equivalent to the inter-rater variability, with a Dice score (DSC) of 0.967 at C2/C3 level. The segmentation performance for grey matter at C2/C3 level was close to inter-rater variability, reaching an accuracy (DSC) of 0.826 for healthy subjects and 0.835 people with clinically isolated syndrome MS.

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Conflict of interest statement

FP, MJC, MY, LH, ET, HK, ML, CWK and SO declare no competing financial interests. DM has received honoraria through payments to his employer, UCL Institute of Neurology, for Advisory Committee and/or Consultancy advice in multiple sclerosis studies from Biogen Idec, GlaxoSmithKline, Novartis, Merck, Chugai, Mitsubishi Pharma Europe and Bayer Schering Pharma. He has also received compensation through payments to my employer for performing central MRI analysis of multiple sclerosis trials from GlaxoSmithKline, Biogen Idec, Novartis and Merck. OC is a consultant for Novartis, Biogen-Idec, Genzyme and General Electric, and all the payments are made to the UCL Institute of Neurology. She is an associate editor of Neurology.

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