  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. We aim to develop an automatic algorithm by deep learning to extract
coronary arteries from ICAs.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. 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.
