Automated detection of vulnerable plaque in intravascular ultrasound images

TL;DR

This paper develops machine learning methods, especially CNN, to automatically classify thin-cap fibroatheroma in intravascular ultrasound images, aiding in the detection of vulnerable plaques related to acute coronary syndrome.

Contribution

It introduces a pixel range based feature extraction method and compares multiple classifiers, identifying CNN as the most accurate for TCFA detection.

Findings

CNN achieved the highest AUC of 0.933

Top features aligned with physician diagnostic criteria

12,325 IVUS images used for training and evaluation

Abstract

Acute Coronary Syndrome (ACS) is a syndrome caused by a decrease in blood flow in the coronary arteries. The ACS is usually related to coronary thrombosis and is primarily caused by plaque rupture followed by plaque erosion and calcified nodule. Thin-cap fibroatheroma (TCFA) is known to be the most similar lesion morphologically to a plaque rupture. In this paper, we propose methods to classify TCFA using various machine learning classifiers including Feed-forward Neural Network (FNN), K-Nearest Neighbor (KNN), Random Forest (RF) and Convolutional Neural Network (CNN) to figure out a classifier that shows optimal TCFA classification accuracy. In addition, we suggest pixel range based feature extraction method to extract the ratio of pixels in the different region of interests to reflect the physician's TCFA discrimination criteria. A total of 12,325 IVUS images were labeled with corresponding OCT images to train and evaluate the classifiers. We achieved 0.884, 0.890, 0.878 and 0.933 Area Under the ROC Curve (AUC) in the order of using FNN, KNN, RF and CNN classifier. As a result, the CNN classifier performed best and the top 10 features of the feature-based classifiers (FNN, KNN, RF) were found to be similar to the physician's TCFA diagnostic criteria.