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

Atefeh Abdolmanafi,Farida Cheriet,Nagib Dahdah,Luc Duong

doi:10.1364/boe.8.001203

Abstract

Kawasaki disease (KD) is an acute childhood disease complicated by coronary artery aneurysms, intima thickening, thrombi, stenosis, lamellar calcifications, and disappearance of the media border. Automatic classification of the coronary artery layers (intima, media, and scar features) is important for analyzing optical coherence tomography (OCT) images recorded in pediatric patients. OCT has been known as an intracoronary imaging modality using near-infrared light which has recently been used to image the inner coronary artery tissues of pediatric patients, providing high spatial resolution (ranging from 10 to 20 μm). This study aims to develop a robust and fully automated tissue classification method by using the convolutional neural networks (CNNs) as feature extractor and comparing the predictions of three state-of-the-art classifiers, CNN, random forest (RF), and support vector machine (SVM). The results show the robustness of CNN as the feature extractor and random forest as the classifier with classification rate up to 96%, especially to characterize the second layer of coronary arteries (media), which is a very thin layer and it is challenging to be recognized and specified from other tissues.

Highlights

The results demonstrate that using the pretrained convolutional neural networks (CNNs) as a feature generator and employing the extracted CNN features to train Random Forest compete against using CNN as the classifier even with deep fine-tuning the network
Automated tissue classification method is proposed in this work by using a pre-trained CNN as feature extractor by removing the classification layers and using the activations of the last fully connected layer to train Random Forest and support vector machine (SVM)
The classifier accuracy sensitivity specificity results confirm the robustness of CNN features to describe the tissue characteristics and Random Forest as the classifier considering the small size of the arteries in children and infants, which is followed by very thin layers in the structure of coronary arteries, and optical coherence tomography (OCT) artifacts

Summary

Introduction

The first layer, the intima, is a transparent, achromatic, and extremely elastic structure comprised of endothelial cells in direct contact with circulating blood. It is characterized by a signal-rich pattern in OCT images, and a normal intima has a reported thickness of 61.7 ± 17.0 μm. The layer of the arterial wall, the media, is homogeneous and composed of smooth muscle cells, lined by the inner and the outer elastica layers which are composed of elastic fibers. The adventitia is the outermost layer of the artery, surrounding the media and characterized by a signal-rich layer in OCT images [1, 2]

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Conclusion