UWAT-GAN: Fundus Fluorescein Angiography Synthesis via Ultra-wide-angle Transformation Multi-scale GAN

TL;DR

This paper introduces UWAT-GAN, a novel multi-scale GAN that synthesizes high-resolution ultra-wide-angle fluorescein angiography images from UWF-SLO images, improving detail capture and reducing the need for invasive procedures.

Contribution

The paper presents a new conditional GAN with multi-scale generators, fusion, and attention modules for high-resolution UWF-FA synthesis, outperforming existing methods.

Findings

UWAT-GAN produces higher-resolution images than prior methods.

The model effectively captures tiny vascular lesions.

Experiments show superior performance on in-house dataset.

Abstract

Fundus photography is an essential examination for clinical and differential diagnosis of fundus diseases. Recently, Ultra-Wide-angle Fundus (UWF) techniques, UWF Fluorescein Angiography (UWF-FA) and UWF Scanning Laser Ophthalmoscopy (UWF-SLO) have been gradually put into use. However, Fluorescein Angiography (FA) and UWF-FA require injecting sodium fluorescein which may have detrimental influences. To avoid negative impacts, cross-modality medical image generation algorithms have been proposed. Nevertheless, current methods in fundus imaging could not produce high-resolution images and are unable to capture tiny vascular lesion areas. This paper proposes a novel conditional generative adversarial network (UWAT-GAN) to synthesize UWF-FA from UWF-SLO. Using multi-scale generators and a fusion module patch to better extract global and local information, our model can generate high-resolution images. Moreover, an attention transmit module is proposed to help the decoder learn effectively. Besides, a supervised approach is used to train the network using multiple new weighted losses on different scales of data. Experiments on an in-house UWF image dataset demonstrate the superiority of the UWAT-GAN over the state-of-the-art methods. The source code is available at: https://github.com/Tinysqua/UWAT-GAN.