  Segmenting coronary arteries is challenging, as classic unsupervised methods
fail to produce satisfactory results and modern supervised learning (deep
learning) requires manual annotation which is often time-consuming and can some
time be infeasible. To solve this problem, we propose a knowledge transfer
based shape-consistent generative adversarial network (SC-GAN), which is an
annotation-free approach that uses the knowledge from publicly available
annotated fundus dataset to segment coronary arteries. The proposed network is
trained in an end-to-end fashion, generating and segmenting synthetic images
that maintain the background of coronary angiography and preserve the vascular
structures of retinal vessels and coronary arteries. We train and evaluate the
proposed model on a dataset of 1092 digital subtraction angiography images, and
experiments demonstrate the supreme accuracy of the proposed method on coronary
arteries segmentation.
