Design And Implementation Of An Artificial Intelligence-Based Coronary Angiography Calcification Scoring

Coronary Heart Disease,abbreviated as CHD,is a serious threat to our citizens’ healthy and life.Coronary artery calcification(CAC)is the most specific pathological feature of CHD.Therefore,automated scoring of coronary artery calcification can improve the prediction of cardiovascular risk events.At present,the distribution of medical resources in China is not balanced.Therefore,intelligent medical system is of great research value.Coronary artery calcification scoring module is also a significant part of coronary artery intelligent recognition and assisted diagnosis system.The main works of this thesis are as follows:Firstly,aiming at the lack of public datasets in the academic community,the thesis constructs a process and specification for creating a calcification dataset based on coronary angiography images.Including the definition,generation steps and specification of key-frame images,labeled images and lesion-section images of different vessels’ calcification.Data volume statistics and quality analysis were also conducted.Secondly,aiming at the problem of difficult localization of calcified lesions in coronary angiography images due to high noise and many interfering terms,a coronary artery backbone vessel segmentation network CDUNet is proposed in this thesis.Inspired by the structure of UNet,CDUNet uses an improved ResNet34 as the backbone network for feature extraction and introduces a boundary refinement module to cascade with it to further improve the network’s image segmentation performance.Through comparison experiments,CDUNet has a certain improvement over UNet and PSPNet in the task of coronary artery backbone vessel segmentation.Thirdly,aiming at the task of coronary angiography calcification scoring,the thesis proposes two methods.In first method,we extract the features of calcified lesions based on radiomics,then use random forest algorithm for regression analysis.In second method,we propose a deep neural network ReSPPNet based on Multi-Task Deep Learning.In terms of network structure,the problem of multi-size input is solved by introducing a spatial pyramid pooling layer.In terms of training approach,the sensitivity of the model to calcified plaque was improved by using a Multi-Task-Multi-Classification pretraining method.The feasibility of the above prediction methods was demonstrated using a medical consistency test,e.g.,Pearson Correlation Coefficient,Cohen’s Kappa Coefficient and Bland-Altman figure.