Sparse intervertebral fence composition for 3D cervical vertebra segmentation. (5th June 2018)

• Record Type: Journal Article
• Authors: Liu, Xinxin; Yang, Jian; Song, Shuang; Cong, Weijian; Jiao, Peifeng; Song, Hong; Ai, Danni; Jiang, Yurong; Wang, Yongtian
• Is Part Of: Physics in medicine & biology. Volume 63:Number 11(2018:Jun.)
• Publication Date: 2018-06-05
• Subjects: computed tomography -- intervertebral fence -- deformable models -- cervical vertebra -- segmentation; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/aac226
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Statistical shape models are capable of extracting shape prior information, and are usually utilized to assist the task of segmentation of medical images. However, such models require large training datasets in the case of multi-object structures, and it also is difficult to achieve satisfactory results for complex shapes. This study proposed a novel statistical model for cervical vertebra segmentation, called sparse intervertebral fence composition (SiFC), which can reconstruct the boundary between adjacent vertebrae by modeling intervertebral fences. The complex shape of the cervical spine is replaced by a simple intervertebral fence, which considerably reduces the difficulty of cervical segmentation. The final segmentation results are obtained by using a 3D active contour deformation model without shape constraint, which substantially enhances the recognition capability of the proposed method for objects with complex shapes. The proposed segmentation framework is tested on a dataset with CT images from 20 patients. A quantitative comparison against corresponding reference vertebral segmentation yields an overall mean absolute surface distance of 0.70 mm and a dice similarity index of 95.47% for cervical vertebral segmentation. The experimental results show that the SiFC method achieves competitive cervical vertebral segmentation performances, and completely eliminates inter-process overlap.