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

Structural constrained virtual histology staining for human coronary imaging using deep learning