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Proceedings Paper

Detection of vertebral degenerative disc disease based on cortical shell unwrapping
Author(s): Hector E. Muñoz; Jianhua Yao; Joseph E. Burns; Ronald M. Summers
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Paper Abstract

Degenerative disc disease (DDD) can be identified as hyperdense regions of bone and osseous spur formation in the spine that become more prevalent with age. These regions can act as confounding factors in the search for alternative hyperdense foci such as neoplastic processes. We created a preliminary CAD system that detects DDD in the spine on CT images. After the spine is segmented, the cortical shell of each vertebral body is unwrapped onto a 2D map. Candidates are detected from the 2D map based on their intensity and gradient. The 2D detections are remapped into 3D space and a level set algorithm is applied to more fully segment the 3D lesions. Features generated from the unwrapped 2D map and 3D segmentation are combined to train a support vector machine (SVM) classifier. The classifier was trained on 20 cases with DDD, which were marked by a radiologist. The pre-SVM program detected 164/193 ground truth lesions. Preliminary results showed 69.65% sensitivity with a 95% confidence interval of (64.47%, 73.92%), at an average of 9.8 false positives per patient.

Paper Details

Date Published: 29 March 2013
PDF: 7 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86700C (29 March 2013); doi: 10.1117/12.2008063
Show Author Affiliations
Hector E. Muñoz, National Institutes of Health Clinical Ctr. (United States)
Jianhua Yao, National Institutes of Health Clinical Ctr. (United States)
Joseph E. Burns, Univ. of California, Irvine (United States)
Ronald M. Summers, National Institutes of Health Clinical Ctr. (United States)

Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)

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