Detection and Classification of Chronic Total Occlusion lesions using Deep Learning.

Cardiovascular disease (CVD) is one of the diseases with the highest mortality rate in modern society, while chronic total occlusion (CTO) is the initial factor that influences the success rate of percutaneous coronary intervention (PCI), which is one of the most common treatments for CVD. In this work, novel deep convolutional neural networks (CNNs) are proposed to detect the entry point of CTO and classify its morphology according to the coronary angiography automatically. Specifically, feature pyramid networks (FPN) module and model fusion technique are applied to the detection network, and data augmentation and attentive regularization loss by reciprocative learning algorithm are used in classification network. Extracted from contrary angiography, the dataset consists of 2059 cases annotated by professional cardiologists. Experiment results show that the recall of CTO detection can reach up to 89.3%, and the sensitivity and specificity of CTO classification can reach up to 94.5% and 89.1% respectively.