Series-Parallel Generative Adversarial Network Architecture for Translating from Fundus Structure Image to Fluorescence Angiography

Although fundus fluorescein angiography (FFA) is a very effective retinal imaging tool for ophthalmic diagnosis, the requirement of intravenous injection of harmful fluorescein dye limits its application. As a screening diagnostic method that reduces the frequency of intravenous injection, a series-parallel generative adversarial network (GAN) architecture for translating fundus structure image to FFA images is proposed herein, using deep learning-based software that only needs an intravenous injection for the training process. Firstly, the fundus structure image and the corresponding FFA images of three phases are collected. Secondly, our series-parallel GAN is trained to translate FFA images from fundus structure image with the supervision of FFA images. Thirdly, the trained series-parallel GAN model is used to translate FFA images by only using fundus structure image. By comparing the FFA images translated by our algorithm, Sequence GAN, pix2pix, and cycleGAN, we show the advancement of our algorithm. To further confirm the advancements of our algorithm, we evaluate the peak signal-to-noise ratio (PSNR), structural similarity (SSIM) index, and mean-squared error (MSE) of our algorithm, Sequence GAN, pix2pix, and cycleGAN. To demonstrate the performance of our method, we show some typical FFA images translated by our algorithm.