  Histopathological analysis is crucial in artery characterization for coronary
artery disease (CAD). However, histology requires an invasive and
time-consuming process. In this paper, we propose to generate virtual histology
staining using Optical Coherence Tomography (OCT) images to enable real-time
histological visualization. We develop a deep learning network, namely
Coronary-GAN, to transfer coronary OCT images to virtual histology images. With
a special consideration on the structural constraints in coronary OCT images,
our method achieves better image generation performance than the conventional
GAN-based method. The experimental results indicate that Coronary-GAN generates
virtual histology images that are similar to real histology images, revealing
the human coronary layers.
