Morphology Reconstruction of Obstructed Coronary Artery in Angiographic Images In cardiac arterial interventional therapy, coronary angiograms provides key information to physicians for treatment strategy selection. However, it is hard to extract the information about arteries with CTO lesion in absence of contrast opacification and visualization of the CTO coronary arteries in angiograms. In this paper, we present an algorithm, which can predict the extension direction of artery with CTO and reconstruct the morphology of blocked artery in coronary angiograms. First, our algorithm segment the CTO artery angiogram and extract the artery skeleton. Second, an iterative approach is preformed to reconstruct the skeleton of blocked artery. Finally, our algorithm generates the simulated postoperative angiogram according to the skeleton image. The results demonstrate that automatic morphology reconstructing of CTO coronary artery with high accuracy and reliability is feasible.

Automated Stenosis Detection and Classification in Xray Angiography Using Deep Neural Network This paper proposes a deeplearning based workflow for stenosis classification and localization on coronary angiography images of 194 patients from a multicenter study. Coronary stenosis severities were categorized into three groups of  25% stenosis as 2CAT for classification training labels; and stenosis bounding boxes were annotated in images as stenosis localization labels, based on expert physicianu0027s visual reading on right coronary artery (RCA) and left coronary artery (LCA) images with full contrast filling of the coronary artery. CNN and recurrent neural network models were employed for coronary artery view classification, candidate frame selection and stenosis classification. Furthermore, stenosis activation maps were implemented for weaklysupervised stenoses positioning. In experiments, our method achieved 0.910.85 AUC values for 3CAT stenosis classification in RCA and LCA respectively; and 0.910.87 AUC values for 2CAT classification in RCA and LCA respectively. For stenosis detection on most significant regions, sensitivity for RCALCA were 0.720.60 respectively; and mean square error between detection and groundtruth center points were 69.679.5 pixels for RCALCA in image with size of  512ast512 . The results show our method achieves high performance in stenosis severity classification and performs reasonably well for stenosis positioning.