title: Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography.
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 [Formula: see text]. 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 [Formula: see text]. Visual identification of lesions with stenosis [Formula: see text] 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.
authors.affiliation: University of Southern California , Department of Electrical Engineering, Los Angeles, California 90089, United States.
authors.forename: Dongwoo
authors.initials: D
authors.lastname: Kang
authors.affiliation: Cedars-Sinai Medical Center , Biomedical Imaging Research Institute, Department of Biomedical Sciences, Los Angeles, California 90048, United States.
authors.forename: Damini
authors.initials: D
authors.lastname: Dey
authors.affiliation: Cedars-Sinai Medical Center , Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Los Angeles, California 90048, United States.
authors.forename: Piotr J
authors.initials: PJ
authors.lastname: Slomka
authors.affiliation: Cedars-Sinai Medical Center , Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Los Angeles, California 90048, United States.
authors.forename: Reza
authors.initials: R
authors.lastname: Arsanjani
authors.affiliation: Cedars-Sinai Medical Center , Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Los Angeles, California 90048, United States.
authors.forename: Ryo
authors.initials: R
authors.lastname: Nakazato
authors.affiliation: University of Southern California , Department of Electrical Engineering, Los Angeles, California 90089, United States.
authors.forename: Hyunsuk
authors.initials: H
authors.lastname: Ko
authors.affiliation: Cedars-Sinai Medical Center , Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Los Angeles, California 90048, United States.
authors.forename: Daniel S
authors.initials: DS
authors.lastname: Berman
authors.affiliation: Cedars-Sinai Medical Center , Biomedical Imaging Research Institute, Department of Biomedical Sciences, Los Angeles, California 90048, United States.
authors.forename: Debiao
authors.initials: D
authors.lastname: Li
authors.affiliation: University of Southern California , Department of Electrical Engineering, Los Angeles, California 90089, United States.
authors.forename: C-C Jay
authors.initials: CC
authors.lastname: Kuo
keywords: coronary arterial disease; coronary arterial lesion detection from coronary computed tomography angiography; coronary computed tomography angiography; image feature extraction; learning-based detection; machine learning; structured learning; support vector machines; support vector regression