A new approach to extracting coronary arteries and detecting stenosis in invasive coronary angiograms

Background: In stable coronary artery disease (CAD), reduction in mortality and/or myocardial infarction with revascularization over medical therapy has not been reliably achieved. Coronary arteries are usually extracted to perform stenosis detection. As such, accurate segmentation of vascular structures and quantification of coronary arterial stenosis in invasive coronary angiograms (ICA) is necessary. However, performing accurate arterial segmentation and stenosis detection automatically remains a great challenge because of confounding factors, low contrast and moving frames in ICA. Purpose: We aim to develop an automatic algorithm by deep learning to extract coronary arteries from ICAs. Additionally, an automatic quantification and detection algorithm will be developed to analyze the stenosis in the coronary arterial tree. Methods: In this study, a multi-input and multi-scale (MIMS) U-Net with a two-stage recurrent training strategy was proposed for the automatic vessel segmentation. Incorporating features such as the Inception residual module with depth-wise separable convolutional layers, the proposed model generated a refined prediction map with the following two training stages: (i) Stage I coarsely segmented the major coronary arteries from pre-processed single-channel ICAs and generated the probability map of vessels; (ii) during the Stage II, a three-channel image consisting of the original preprocessed image, a generated probability map, and an edge-enhanced image generated from the preprocessed image was fed to the proposed MIMS U-Net to produce the final segmentation probability map. During the training stage, the probability maps were iteratively and recurrently updated by feeding into the neural network. After segmentation, an arterial stenosis detection algorithm was developed to extract vascular centerlines and calculate arterial diameters to evaluate stenotic level. Results and Conclusions: Experimental results demonstrated that the proposed method achieved an average Dice score of 0.8329, an average sensitivity of 0.8281, and an average specificity of 0.9979 in our dataset with 294 ICAs obtained from 73 patient. Moreover, our stenosis detection algorithm achieved a true positive rate of 0.6668 and a positive predictive value of 0.7043. Of note, our large image dataset covers the most commonly used view angles in clinical practice of ICA. Accordingly, our proposed approach has great promise to clinical use and could help physicians improve diagnosis and therapeutic decisions for CAD