Classification of coronary artery tissues using optical coherence tomography imaging in Kawasaki disease

Abstract

Intravascular imaging modalities, such as Optical Coherence Tomography (OCT) allow nowadays improving diagnosis, treatment, follow-up, and even prevention of coronary artery disease in the adult. OCT has been recently used in children following Kawasaki disease (KD), the most prevalent acquired coronary artery disease during childhood with devastating complications. The assessment of coronary artery layers with OCT and early detection of coronary sequelae secondary to KD is a promising tool for preventing myocardial infarction in this population. More importantly, OCT is promising for tissue quantification of the inner vessel wall, including neo intima luminal myofibroblast proliferation, calcification, and fibrous scar deposits. The goal of this study is to classify the coronary artery layers of OCT imaging obtained from a series of KD patients. Our approach is focused on developing a robust Random Forest classifier built on the idea of randomly selecting a subset of features at each node and based on second-and higher-order statistical texture analysis which estimates the gray-level spatial distribution of images by specifying the local features of each pixel and extracting the statistics from their distribution. The average classification accuracy for intima and media are 76.36% and 73.72% respectively. Random forest classifier with texture analysis promises for classification of coronary artery tissue.

Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography

“…Our results show that it is more efficient to use pre-trained CNNs as feature generators for our application by removing the classification layer and using the activations of the last fully connected layer to train Random Forest. By comparing the results of tissue classification using CNN features against our previous work [38], CNN features are substantially robust to Fig. 7.…”

“…We also determine if it is better to fine-tune a pre-trained network and use it as the classifier or applying pre-trained CNNs as feature extractor and using the activations of the last fully connected layer to train other classifiers in our application. Finally, we analyze the performance of the classifiers using CNN features and compare the results against tissue classification results of coronary arteries using texture analysis, which is recently done by our group [38].…”