Bridging the resources gap: deep learning for fluorescein angiography and optical coherence tomography macular thickness map image translation

Abstract

Abstract Background To assess the ability of the pix2pix generative adversarial network (pix2pix GAN) to synthesize clinically useful optical coherence tomography (OCT) color-coded macular thickness maps based on a modest-sized original fluorescein angiography (FA) dataset and the reverse, to be used as a plausible alternative to either imaging technique in patients with diabetic macular edema (DME). Methods Original images of 1,195 eyes of 708 nonconsecutive diabetic patients with or without DME were retrospectively analyzed. OCT macular thickness maps and corresponding FA images were preprocessed for use in training and testing the proposed pix2pix GAN. The best quality synthesized images using the test set were selected based on the Fréchet inception distance score, and their quality was studied subjectively by image readers and objectively by calculating the peak signal-to-noise ratio, structural similarity index, and Hamming distance. We also used original and synthesized images in a trained deep convolutional neural network (DCNN) to plot the difference between synthesized images and their ground-truth analogues and calculate the learned perceptual image patch similarity metric. Results The pix2pix GAN-synthesized images showed plausible subjectively and objectively assessed quality, which can provide a clinically useful alternative to either image modality. Conclusion Using the pix2pix GAN to synthesize mutually dependent OCT color-coded macular thickness maps or FA images can overcome issues related to machine unavailability or clinical situations that preclude the performance of either imaging technique. Trial registration ClinicalTrials.gov Identifier: NCT05105620, November 2021. “Retrospectively registered”.