Fully automated segmentation of the cervical cord from T1-weighted MRI using PropSeg: Application to XXXX .

@entity1 @entity194 @entity717 @entity15 @entity3457 @entity922
Spinal cord (SC) @entity194 , 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 @entity3457 ( @entity3457 ) method, in healthy controls (HC) and @entity1 with @entity15 ( @entity15 ). MRI data from 120 @entity1 were retrospectively analysed; 26 HC, 21 with @entity922 , 26 relapsing remitting @entity15 , 26 primary and 21 secondary progressive @entity15 . MRI data from 40 @entity1 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 @entity3457 method, the reproducibility of CSA measurements obtained with PropSeg was high, both in @entity1 and in HC, with ICC  >  0.98 in all cases. There was no significant difference between PropSeg and @entity3457 in terms of detecting change over time. Furthermore, PropSeg provided measures that correlated with @entity717 , similar to the @entity3457 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 @entity15 .
