Automatic detection of coronary artery stenosis by convolutional neural network with temporal constraint.

Coronary artery disease (CAD) is a major threat to human health. In clinical practice, X-ray coronary angiography remains the gold standard for CAD diagnosis, where the detection of stenosis is a crucial step. However, detection is challenging due to the low contrast between vessels and surrounding tissues as well as the complex overlap of background structures with inhomogeneous intensities. To achieve automatic and accurate stenosis detection, we propose a convolutional neural network-based method with a novel temporal constraint across X-ray angiographic sequences. Specifically, we develop a deconvolutional single-shot multibox detector for candidate detection on contrast-filled X-ray frames selected by U-Net. Based on these static frames, the detector demonstrates high sensitivity for stenoses yet unacceptable false positives still exist. To solve this problem, we propose a customized seq-fps module that exploits the temporal consistency of consecutive frames to reduce the number of false positives. Experiments are conducted with 148 X-ray angiographic sequences. The results show that the proposed method outperforms existing stenosis detection methods, achieving the highest sensitivity of 87.2% and positive predictive value of 79.5%. Furthermore, this study provides a promising tool to improve CAD diagnosis in clinical practice.