  There has been a significant increase from 2010 to 2016 in the number of
people suffering from spine problems. The automatic image segmentation of the
spine obtained from a computed tomography (CT) image is important for
diagnosing spine conditions and for performing surgery with computer-assisted
surgery systems. The spine has a complex anatomy that consists of 33 vertebrae,
23 intervertebral disks, the spinal cord, and connecting ribs. As a result, the
spinal surgeon is faced with the challenge of needing a robust algorithm to
segment and create a model of the spine. In this study, we developed an
automatic segmentation method to segment the spine, and we compared our
segmentation results with reference segmentations obtained by experts. We
developed a fully automatic approach for spine segmentation from CT based on a
hybrid method. This method combines the convolutional neural network (CNN) and
fully convolutional network (FCN), and utilizes class redundancy as a soft
constraint to greatly improve the segmentation results. The proposed method was
found to significantly enhance the accuracy of the segmentation results and the
system processing time. Our comparison was based on 12 measurements: the Dice
coefficient (94%), Jaccard index (93%), volumetric similarity (96%),
sensitivity (97%), specificity (99%), precision (over segmentation; 8.3 and
under segmentation 2.6), accuracy (99%), Matthews correlation coefficient
(0.93), mean surface distance (0.16 mm), Hausdorff distance (7.4 mm), and
global consistency error (0.02). We experimented with CT images from 32
patients, and the experimental results demonstrated the efficiency of the
proposed method.
