Journal
SCIENTIFIC REPORTS
Volume 6, Issue -, Pages -Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/srep36151
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Funding
- National Institute for Health Research University College London Hospitals Biomedical Research Centre (NIHR BRC UCLH/UCL High Impact Initiative) [BW.mn.BRC10269]
- EPSRC [EP/H046410/1, EP/J020990/1, EP/K005278]
- MRC [MR/J01107X/1]
- NIHR Biomedical Research Unit (Dementia) at UCL
- NIHR BRC UCLH/UCL [BW.mn.BRC10269, RD03/10/RAG0449]
- Swiss National Science Foundation [P2EZP3_148749, P300PB_161087]
- UCL
- Neuroscience Center Zurich
- UK Multiple Sclerosis Society
- Medical Research Council
- UK Multiple Sclerosis Society [892/08]
- Brain Research Trust
- EPSRC [EP/J020990/1, EP/H046410/1] Funding Source: UKRI
- MRC [MR/J01107X/1] Funding Source: UKRI
- Swiss National Science Foundation (SNF) [P300PB_161087, P2EZP3_148749] Funding Source: Swiss National Science Foundation (SNF)
- Engineering and Physical Sciences Research Council [EP/H046410/1, EP/J020990/1] Funding Source: researchfish
- Medical Research Council [MR/J01107X/1] Funding Source: researchfish
- National Institute for Health Research [NF-SI-0508-10058] Funding Source: researchfish
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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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