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

Automatic Spine Segmentation using Convolutional Neural Network via Redundant Generation of Class Labels for 3D Spine Modeling