Image Processing

Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography

[+] Author Affiliations
Dongwoo Kang, Hyunsuk Ko, C.-C. Jay Kuo

University of Southern California, Department of Electrical Engineering, Los Angeles, California 90089, United States

Damini Dey, Debiao Li

Cedars-Sinai Medical Center, Biomedical Imaging Research Institute, Department of Biomedical Sciences, Los Angeles, California 90048, United States

Piotr J. Slomka, Reza Arsanjani, Ryo Nakazato, Daniel S. Berman

Cedars-Sinai Medical Center, Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Los Angeles, California 90048, United States

J. Med. Imag. 2(1), 014003 (Mar 06, 2015). doi:10.1117/1.JMI.2.1.014003
History: Received June 26, 2014; Accepted February 11, 2015
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Abstract.  Visual identification of coronary arterial lesion from three-dimensional coronary computed tomography angiography (CTA) remains challenging. We aimed to develop a robust automated algorithm for computer detection of coronary artery lesions by machine learning techniques. A structured learning technique is proposed to detect all coronary arterial lesions with stenosis 25%. Our algorithm consists of two stages: (1) two independent base decisions indicating the existence of lesions in each arterial segment and (b) the final decision made by combining the base decisions. One of the base decisions is the support vector machine (SVM) based learning algorithm, which divides each artery into small volume patches and integrates several quantitative geometric and shape features for arterial lesions in each small volume patch by SVM algorithm. The other base decision is the formula-based analytic method. The final decision in the first stage applies SVM-based decision fusion to combine the two base decisions in the second stage. The proposed algorithm was applied to 42 CTA patient datasets, acquired with dual-source CT, where 21 datasets had 45 lesions with stenosis 25%. Visual identification of lesions with stenosis 25% by three expert readers, using consensus reading, was considered as a reference standard. Our method performed with high sensitivity (93%), specificity (95%), and accuracy (94%), with receiver operator characteristic area under the curve of 0.94. The proposed algorithm shows promising results in the automated detection of obstructive and nonobstructive lesions from CTA.

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© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Dongwoo Kang ; Damini Dey ; Piotr J. Slomka ; Reza Arsanjani ; Ryo Nakazato, et al.
"Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography", J. Med. Imag. 2(1), 014003 (Mar 06, 2015). ; http://dx.doi.org/10.1117/1.JMI.2.1.014003


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