Real-Time Coronary Artery Stenosis Detection Based on Modern Neural Networks

Abstract

Invasive coronary angiography remains the gold standard for diagnosing coronary artery disease, which may be complicated by both, patient-specific anatomy and image quality. Deep learning techniques aimed at detecting coronary artery stenoses may facilitate the diagnosis. However, previous studies have failed to achieve superior accuracy and performance for real-time labeling. Our study is aimed at confirming the feasibility of real-time coronary artery stenosis detection using deep learning methods. To reach this goal we trained and tested eight promising detectors based on different neural network architectures (MobileNet, ResNet-50, ResNet-101, Inception ResNet, NASNet) using clinical angiography data of 100 patients.Three neural networks have demonstrated superior results. The network based on Faster-RCNN Inception ResNet V2 is the most accurate and it achieved the mean Average Precision of 0.954 and the prediction rate of 3 fps on the validation subset. The relatively lightweight SSD MobileNet V2 network proved itself as the fastest one with an mAP of 0.830 and a mean prediction rate of 38 fps. The model based on RFCN ResNet-101 V2 has demonstrated an optimal accuracy-to-speed ratio. Its mAP makes up 0.94, while the prediction speed is 10 fps.The resultant performance-accuracy balance of the modern neural networks has confirmed the feasibility of real-time coronary artery stenosis detection supporting the decision-making process of the Heart Team interpreting coronary angiography findings.

Two New Stenosis Detection Methods of Coronary Angiograms

“…As for automatic detection of stenoses, the most popular way is using machine learning related methods nowadays. For example, [4] confirmed the feasibility of real-time coronary artery stenosis detection by training and testing eight promising detectors based on different neural network architectures. [21] proposed a convolutional neural network-based method with a novel temporal constraint across X-ray angiographic sequences and it demonstrates high sensitivity for stenoses.…”