Vertebral degenerative disc disease severity evaluation using random forest classification

Hector E. Munoz; Jianhua Yao; Joseph E. Burns; Yasuyuki Pham; James Stieger; Ronald M. Summers

doi:10.1117/12.2042793

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18 March 2014 Vertebral degenerative disc disease severity evaluation using random forest classification

Hector E. Munoz, Jianhua Yao, Joseph E. Burns, Yasuyuki Pham, James Stieger, Ronald M. Summers

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Proceedings Volume 9035, Medical Imaging 2014: Computer-Aided Diagnosis; 90353A (2014) https://doi.org/10.1117/12.2042793
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Degenerative disc disease (DDD) develops in the spine as vertebral discs degenerate and osseous excrescences or outgrowths naturally form to restabilize unstable segments of the spine. These osseous excrescences, or osteophytes, may progress or stabilize in size as the spine reaches a new equilibrium point. We have previously created a CAD system that detects DDD. This paper presents a new system to determine the severity of DDD of individual vertebral levels. This will be useful to monitor the progress of developing DDD, as rapid growth may indicate that there is a greater stabilization problem that should be addressed. The existing DDD CAD system extracts the spine from CT images and segments the cortical shell of individual levels with a dual-surface model. The cortical shell is unwrapped, and is analyzed to detect the hyperdense regions of DDD. Three radiologists scored the severity of DDD of each disc space of 46 CT scans. Radiologists’ scores and features generated from CAD detections were used to train a random forest classifier. The classifier then assessed the severity of DDD at each vertebral disc level. The agreement between the computer severity score and the average radiologist’s score had a quadratic weighted Cohen’s kappa of 0.64.

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Hector E. Munoz, Jianhua Yao, Joseph E. Burns, Yasuyuki Pham, James Stieger, and Ronald M. Summers "Vertebral degenerative disc disease severity evaluation using random forest classification", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90353A (18 March 2014); https://doi.org/10.1117/12.2042793

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