Synthesizing multi-frame high-resolution fluorescein angiography images from retinal fundus images using generative adversarial networks.

ABSTRACT:

Background

Fundus fluorescein angiography (FA) can be used to diagnose fundus diseases by observing dynamic fluorescein changes that reflect vascular circulation in the fundus. As FA may pose a risk to patients, generative adversarial networks have been used to convert retinal fundus images into fluorescein angiography images. However, the available methods focus on generating FA images of a single phase, and the resolution of the generated FA images is low, being unsuitable for accurately diagnosing fundus diseases.

Methods

We propose a network that generates multi-frame high-resolution FA images. This network consists of a low-resolution GAN (LrGAN) and a high-resolution GAN (HrGAN), where LrGAN generates low-resolution and full-size FA images with global intensity informa

SUBMITTER: Li P 

PROVIDER: S-EPMC9945680 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature