title: Fully automated segmentation of the cervical cord from T1-weighted MRI using PropSeg: Application to multiple sclerosis.
abstract: Spinal cord (SC) atrophy, i.e. a reduction in the SC cross-sectional area (CSA) over time, can be measured by means of image segmentation using magnetic resonance imaging (MRI). However, segmentation methods have been limited by factors relating to reproducibility or sensitivity to change. The purpose of this study was to evaluate a fully automated SC segmentation method (PropSeg), and compare this to a semi-automated active surface (AS) method, in healthy controls (HC) and people with multiple sclerosis (MS). MRI data from 120 people were retrospectively analysed; 26 HC, 21 with clinically isolated syndrome, 26 relapsing remitting MS, 26 primary and 21 secondary progressive MS. MRI data from 40 people returning after one year were also analysed. CSA measurements were obtained within the cervical SC. Reproducibility of the measurements was assessed using the intraclass correlation coefficient (ICC). A comparison between mean CSA changes obtained with the two methods over time was performed using multivariate structural equation regression models. Associations between CSA measures and clinical scores were investigated using linear regression models. Compared to the AS method, the reproducibility of CSA measurements obtained with PropSeg was high, both in patients and in HC, with ICC > 0.98 in all cases. There was no significant difference between PropSeg and AS in terms of detecting change over time. Furthermore, PropSeg provided measures that correlated with physical disability, similar to the AS method. PropSeg is a time-efficient and reliable segmentation method, which requires no manual intervention, and may facilitate large multi-centre neuroprotective trials in progressive MS.
authors.affiliation: NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK.
authors.forename: Marios C
authors.initials: MC
authors.lastname: Yiannakas
authors.affiliation: NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK.
authors.forename: Ahmed M
authors.initials: AM
authors.lastname: Mustafa
authors.affiliation: Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.
authors.forename: Benjamin
authors.initials: B
authors.lastname: De Leener
authors.affiliation: NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK.
authors.forename: Hugh
authors.initials: H
authors.lastname: Kearney
authors.affiliation: NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK.
authors.forename: Carmen
authors.initials: C
authors.lastname: Tur
authors.affiliation: NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK; Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.
authors.forename: Daniel R
authors.initials: DR
authors.lastname: Altmann
authors.affiliation: NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK.
authors.forename: Floriana
authors.initials: F
authors.lastname: De Angelis
authors.affiliation: NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK.
authors.forename: Domenico
authors.initials: D
authors.lastname: Plantone
authors.affiliation: NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK.
authors.forename: Olga
authors.initials: O
authors.lastname: Ciccarelli
authors.affiliation: NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK.
authors.forename: David H
authors.initials: DH
authors.lastname: Miller
authors.affiliation: Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada; Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada.
authors.forename: Julien
authors.initials: J
authors.lastname: Cohen-Adad
authors.affiliation: NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK; Brain Connectivity Center, C. Mondino National Neurological Institute, Pavia, Italy.
authors.forename: Claudia A M
authors.initials: CA
authors.lastname: Gandini Wheeler-Kingshott
mesh_terms: D000328:Adult; D001284:Atrophy; D066193:Cervical Cord / Q000473:pathology*; D001330:Electronic Data Processing; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009103:Multiple Sclerosis / Q000473:pathology*; D015203:Reproducibility of Results; D012189:Retrospective Studies
keywords: Cord cross-sectional area; Grey matter; Image segmentation; Magnetic resonance imaging; White matter