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

TL;DR

This paper introduces a CNN-based approach for automatic plaque characterization in intracoronary OCT images, simplifying the process by directly classifying images in a single step, and demonstrating promising accuracy for clinical use.

Contribution

The study presents a novel CNN method that directly classifies OCT images for plaque characterization, eliminating multi-step traditional feature-based processes.

Findings

Average prediction accuracy of 0.866 on 269 OCT images

Single-step classification improves efficiency over traditional methods

Potential for clinical translation of automated plaque analysis

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.