Coronary Artery Centerline Extraction Based On Deep Learning And Variational Method

With the continuous development of technology and economy,our lives have been drastically improved.However,due to the changes in people’s dietary structure and the acceleration of the pace of life,the prevalence of coronary heart disease in our country is in a continuous rising stage.Coronary atherosclerotic heart disease(coronary heart disease)is a symptom of myocardial ischemic necrosis caused by the stenosis and obstruction of coronary arteries.Medical image analysis is a clinical auxiliary diagnostic method for coronary heart disease.The coronary artery centerline accurately extracted through medical images is of great significance to the prevention,diagnosis and treatment of coronary heart disease.However,due to the existence of severe calcification,occlusion,imaging artifacts and insufficient contrast agent,automatic centerline extraction is challenging.Most existing methods can only track three major coronary artery centers.This paper proposes a method for extracting the centerline of coronary artery based on deep learning and variational method.First,we use a deep learning segmentation network to segment the coronary arteries in the CTA image end-to-end.Next,we extract the skeleton of the coronary artery segmentation result and use the Euclidean distance minimum spanning tree algorithm to connect the broken skeleton as the initial centerline of the coronary artery.Finally,we optimize the initial centerline by fast marching algorithm and variational method to get the final coronary artery centerline.Experimental results show that the method proposed in this paper can extract a more complete and more accurate coronary artery centerline,including the distal ends of coronary collaterals.The extracted centerline can basically cover all visible coronary artery branches in the CTA image.The innovations of this article include:(1)A segmentation algorithm with high accuracy is designed to obtain the coronary artery segmentation mask;(2)The complete centerline can be automatically obtained through the coronary artery segmentation mask without manually setting the start and end points;(3)An optimization method based on the variational method and fastmarching algorithm is designed to optimize the coronary artery centerline.