Automatic spine segmentation from CT images using Convolutional Neural Network via redundant generation of class labels

Authors
Vania, MalindaMureja, DawitLee, Deukhee
Issue Date
2019-04
Publisher
OXFORD UNIV PRESS
Citation
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING, v.6, no.2, pp.224 - 232
Abstract
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 a fully automatic segmentation method to segment the spine from CT images, and we compared our segmentation results with reference segmentations obtained by well-known methods. We use 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. (C) 2019 Society for Computational Design and Engineering. Publishing Services by Elsevier.
Keywords
Automatic image segmentation; Computed tomography images; Convolutional neural network; Spine segmentation
ISSN
2288-5048
URI
https://pubs.kist.re.kr/handle/201004/120142
DOI
10.1016/j.jcde.2018.05.002
Appears in Collections:
KIST Article > 2019
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