Convolutional neural network based automatic plaque characterization for intracoronary optical coherence tomography images

Shenghua He; Jie Zheng; Akiko Maehara; Gary Mintz; Dalin Tang; Mark Anastasio; Hua Li

doi:10.1117/12.2293957

2 March 2018 Convolutional neural network based automatic plaque characterization for intracoronary optical coherence tomography images

Shenghua He, Jie Zheng, Akiko Maehara, Gary Mintz, Dalin Tang, Mark Anastasio, Hua Li

Author Affiliations +

Proceedings Volume 10574, Medical Imaging 2018: Image Processing; 1057432 (2018) https://doi.org/10.1117/12.2293957
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

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.

Citation Download Citation

Shenghua He, Jie Zheng, Akiko Maehara, Gary Mintz, Dalin Tang, Mark Anastasio, and Hua Li "Convolutional neural network based automatic plaque characterization for intracoronary optical coherence tomography images", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 1057432 (2 March 2018); https://doi.org/10.1117/12.2293957

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available