MCascade R-CNN: A Modified Cascade R-CNN for Detection of Calcified on Coronary Artery Angiography Images

Abstract

Among cardiovascular diseases, coronary artery calcification (CAC) is a high-risk factor for worsening protopathy and increased mortality. However, the coronary artery an-giogram, which is the main approach for CAC diagnosis, suffers from plenty of photographing noise. This brings difficulties to detect calcification from the background. In this paper, a modified Cascade R-CNN (MCascade R-CNN) network is proposed to deal with the problem of calcium detection in angiograms. In the proposed network, we propose an innovative balanced aggregation pyramid structure, integrating multi-level features of every depth in the feature map, based on enhanced propagation of strong semantic features. In addition, a new convolutional attention mechanism is designed to improve the performance of the detector. Experiments show that the proposed method enjoys better performance in detecting and marking CAC in angiograms,