Coronary X-ray Angiography Image Segmentation Methods Based On Deep Learning

Many medical reports pointed out that cardiovascular disease is one of the major diseases that threaten people’s life safety in the past 20 years.Therefore,the prevention and treatment level of cardiovascular disease is closely related to the quality of life.The coronary x-ray an-giography image is an important basis for the analysis and diagnosis of coronary-related dis-eases.The subsequent analysis and diagnosis process depends on the technology of extracting vessel trees from coronary angiography images.The artificial extraction of vessel trees is time-consuming and hard,which can not meet clinical requirements,so automatic methods of vessel segmentation are needed.The segmentation methods of coronary angiography images based on deep learning are studied in this thesis.In this thesis,three deep learning models are designed in two aspects to segment the coro-nary angiography images.Firstly,a single image is used for the end-to-end segment.The angiography images often contain noise,which leads to fuzzy vessel boundaries and manual labelling errors.In order to emphasize the connectivity of the vessel tree,a segmentation model based on multi-task learning of the centerline is proposed.Skip attention connections and cen-terline multi-task learning module are adopted in this model which keeps UNet as the backbone.The model’s excellent performance in the task of vessel segmentation is verified by ablation and comparative experiments,compared with the baseline model DSC increased by 1.07,the sen-sitivity increased by 1.81.Secondly,integrating the sequence information in the angiography sequence into the frame segmentation network.The sequence of coronary angiography images can provide context information similar to the video sequence.Simultaneously,some vessels can not be properly segmented due to the contrast agent’s inhomogeneous flow.Based on the above two reasons,3D-XNet and M2D-XNet are proposed in this thesis.Both models adopt dual network architecture to extract sequence features from the sequence network and integrate them into the frame network.And some modules,including the feature fusion module,feature sift module,and context fusion module,are proposed in these two models to extract context features effectively.Both models show excellent performance and segment the whole vessel well in experiments,AUC can reach 98.56 and DSC can reach 85.64.Finally,a summary of this work is conducted and the future work of coronary angiography image segmentation based on the knowledge of deep learning is analyzed.