Analysis methods of coronary artery intravascular images: A review

Coronary artery disease is among one of the diseases human suffer most. Intravascular coronary arterial image analysis consists of denoising, segmentation, detection, and three-dimensional reconstruction, having a significant meaning for auxiliary diagnosis and treatment of coronary artery disease. Intravascular ultrasound (IVUS) and intravascular optical coherence tomography (IVOCT) are the two most commonly applied intravascular coronary arterial imaging techniques. Based on these fundamental imaging techniques, in recent years, many advanced technologies from traditional machine learning algorithms to deep learning methods were employed in the analysis of intravascular coronary arterial images and made huge progress in this field. In this survey, we reviewed more than one hundred papers published in top journals or conferences such as Neural Networks and MICCAI. These papers proposed approaches or schemes for the intravascular coronary arterial image analysis, including lumen border segmentation, atherosclerotic plaque characterization, media-adventitia segmentation, stent strut detection, and three-dimensional reconstruction. Our survey began with introducing coronary artery intravascular imaging techniques, essential neural networks, and deep learning and then presented an across-the-board review of methods, applications, and trends of intravascular image analysis. This survey is more comprehensive than other articles not only for its scope and reference number but also for discussing the future direction in this field. Compared to other review papers in this field, this article could assist beginners in constructing a basic knowledge frame of coronary artery intravascular image analysis methods and brought state-of-the-art progress in this field to fellow researchers. We hope this paper could benefit either the beginners for coronary arterial image analysis or experienced researchers.