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Proceedings Paper

Direct spondylolisthesis identification and measurement in MR/CT using detectors trained by articulated parameterized spine model
Author(s): Yunliang Cai; Stephanie Leung; James Warrington; Sachin Pandey; Olga Shmuilovich; Shuo Li
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Paper Abstract

The identification of spondylolysis and spondylolisthesis is important in spinal diagnosis, rehabilitation, and surgery planning. Accurate and automatic detection of spinal portion with spondylolisthesis problem will significantly reduce the manual work of physician and provide a more robust evaluation for the spine condition. Most existing automatic identification methods adopted the indirect approach which used vertebrae locations to measure the spondylolisthesis. However, these methods relied heavily on automatic vertebra detection which often suffered from the pool spatial accuracy and the lack of validated pathological training samples. In this study, we present a novel spondylolisthesis detection method which can directly locate the irregular spine portion and output the corresponding grading. The detection is done by a set of learning-based detectors which are discriminatively trained by synthesized spondylolisthesis image samples. To provide sufficient pathological training samples, we used a parameterized spine model to synthesize different types of spondylolysis images from real MR/CT scans. The parameterized model can automatically locate the vertebrae in spine images and estimate their pose orientations, and can inversely alter the vertebrae locations and poses by changing the corresponding parameters. Various training samples can then be generated from only a few spine MR/CT images. The preliminary results suggest great potential for the fast and efficient spondylolisthesis identification and measurement in both MR and CT spine images.

Paper Details

Date Published: 24 February 2017
PDF: 8 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 1013319 (24 February 2017); doi: 10.1117/12.2254072
Show Author Affiliations
Yunliang Cai, Thayer School of Engineering at Dartmouth (United States)
Western Univ. (Canada)
Stephanie Leung, Western Univ. (Canada)
James Warrington, Western Univ. (Canada)
Sachin Pandey, Western Univ. (Canada)
Olga Shmuilovich, Western Univ. (Canada)
Shuo Li, Western Univ. (Canada)

Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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