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.
Yunliang Cai, Stephanie Leung, James Warrington, Sachin Pandey, Olga Shmuilovich, and Shuo Li, "Direct spondylolisthesis identification and measurement in MR/CT using detectors trained by articulated parameterized spine model," Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 1013319 (Presented at SPIE Medical Imaging: February 14, 2017; Published: 24 February 2017); https://doi.org/10.1117/12.2254072.
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