Applying a Pix2Pix Generative Adversarial Network to a Fourier-Domain Optical Coherence Tomography System for Artifact Elimination
The presence of artifacts, including conjugate, DC, and auto-correlation artifacts, is a critical limitation of Fourier-domain optical coherence tomography (FD-OCT). Many methods have been proposed to resolve this problem to obtain high-quality images. Furthermore, the development of deep learning has resulted in many prospective advancements in the medical field; image-to-image translation by using generative adversarial networks (GANs) is one such advancement. In this study, we propose applying the Pix2Pix GAN to eliminate artifacts from FD-OCT images. The first experiment results showed that the proposed framework could translate conventional FD-OCT depth profiles into artifact-free FD-OCT depth profiles. In addition, the FD-OCT ...
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