Research On Deep Learning Based Coronary Artery Segmentation In CT

Today,cardiovascular disease(CVD)accounting for more than 40% disease death in our country,and coronary artery disease(CAD)has been the leading cause of death compared to other CVDs.Therefore,research on CAD detection in early stage should be attached great importance.As an effective non-invasive coronary artery imaging technique,automatic screening of Coronary Computed Tomography Angiography(CCTA)data relies on the high precise segmentation of coronary artery lumen.Based on this requirement,deep learning based coronary Artery segmentation algorithms in CT data was investigated in this paper on the basis of a relatively complete survey.The specific research contents are as follows:First of all,the research on coronary artery lumen segmentation(CALS)method in CCTA data in recent 20 years was comprehensively reviewed.And these methods were divided into several mainstream research directions that have been widely implemented,which respectively are: CALS based on vesselness filtering,CALS based on deformable model,CALS based on machine learning and CALS based on vascular tracking.These methods have their own emphases and are often combined in practice to achieve better segmentation result.Among them,the methods based on deep learning proposed a new perspective of CALS research and has been increasingly applied in this in recent years.However,without prior information and shape restrain about coronary artery,such methods often provide some error result.Then,a CCTA dataset with 81 instances and corresponding pixel-wised segmentation label has been created in the research of this paper.In this local dataset(LD81),CALS algorithms in in original coordinate space has been studied,end-to-end segmentation models based on 2D and 3D data structures were established and compared.Results show that compared with the2 D model,3D data sampling and the corresponding U-net model achieves better segmentation.In addition,CALS based on Polar CPR spatial transformation was investigated in the training set of the Coronary Artery Stenoses Detection and Quantification Evaluation Framework(CASD13)which is the only publicly dataset with accurate lumen boundary label.In this research direction,two existing segmentation algorithms was implemented,and on this basis,a sub-voxel CALS model based on centerline prior information and Polar CPR space transformation was proposed.In this model circle convolution(Circ Conv)is applied to aggregate contour features in different scales,and resulted with an accuracy and smooth segmentation vascular lumen surface.The research in this paper shows that compared with the end-to-end segmentation in the original CCTA coordinates,the CALS method proposed in this study based on its vascular centerline prior has better performance.The segmentation result can reach higher precision in sub-pixel level without complicated post-processing,meet the requirements of down-stream applications and has greater potential and expansion space.