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Proceedings Paper

Convolutional neural network based automatic plaque characterization for intracoronary optical coherence tomography images
Author(s): Shenghua He; Jie Zheng; Akiko Maehara; Gary Mintz; Dalin Tang; Mark Anastasio; Hua Li
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Paper Abstract

Optical coherence tomography (OCT) can provide high-resolution cross-sectional images for analyzing superficial plaques in coronary arteries. Commonly, plaque characterization using intra-coronary OCT images is performed manually by expert observers. This manual analysis is time consuming and its accuracy heavily relies on the experience of human observers. Traditional machine learning based methods, such as the least squares support vector machine and random forest methods, have been recently employed to automatically characterize plaque regions in OCT images. Several processing steps, including feature extraction, informative feature selection, and final pixel classification, are commonly used in these traditional methods. Therefore, the final classification accuracy can be jeopardized by error or inaccuracy within each of these steps. In this study, we proposed a convolutional neural network (CNN) based method to automatically characterize plaques in OCT images. Unlike traditional methods, our method uses the image as a direct input and performs classification as a single- step process. The experiments on 269 OCT images showed that the average prediction accuracy of CNN-based method was 0.866, which indicated a great promise for clinical translation.

Paper Details

Date Published: 2 March 2018
PDF: 7 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 1057432 (2 March 2018); doi: 10.1117/12.2293957
Show Author Affiliations
Shenghua He, Washington Univ. in St. Louis (United States)
Jie Zheng, Washington Univ. School of Medicine in St. Louis (United States)
Akiko Maehara, Cardiovascular Research Foundation (United States)
Gary Mintz, Cardiovascular Research Foundation (United States)
Dalin Tang, Worcester Polytechnic Institute (United States)
Mark Anastasio, Washington Univ. in St. Louis (United States)
Washington Univ. School of Medicine in St. Louis (United States)
Hua Li, Washington Univ. School of Medicine in St. Louis (United States)

Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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