Paper Info
Title
Convolutional neural network based automatic plaque characterization for intracoronary optical coherence tomography images.
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.
Year
DOI
Venue
2018
10.1117/12.2293957
Proceedings of SPIE
Keywords
DocType
Volume
Convolutional neural network,automatic plaque characterization,optical coherence tomography
Journal
10574
ISSN
Citations 
PageRank 
0277-786X
4
0.43
References 
Authors
0
7
Name
Order
Citations
PageRank
Shenghua He191.88
Jie Zheng282.91
Akiko Maehara351.40
Gary S Mintz451.06
Dalin Tang5104.95
Mark A. Anastasio610525.53
Hua Li7459.03