Quantification of Coronary Artery Calcificatiion Scores Based on Deep Learning

• 1、School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876

Abstract：Coronary Heart Disease, abbreviated as CHD, is a serious threat to our citizens\' healthy and life.Coronary artery calcification, abbreviated as CAC, is the most specific pathological feature of CHD. Therefore, automated scoring of coronary artery calcification can improve the prediction of cardiovascular risk events.In this paper, we propose a quantification method of coronary artery calcification scores based on deep learning. Firstly, aiming at the lack of public datasets in the academic community, the paper constructs a process and specification for creating a calcification dataset based on coronary angiography images. Secondly, aiming at the task of coronary angiography calcification scoring, the paper proposes ReSPPNet, a residual network based on spatial pyramid pooling with optimized training method. The problem of multi-size input is solved by introducing a spatial pyramid pooling layer, while being able to fuse global information. Lastly, the feasibility of the above prediction methods was demonstrated using a medical consistency test.

Keywords： deep learning coronary angiography calcification score consistency checking