UWAT-GAN: Fundus Fluorescein Angiography Synthesis via Ultra-wide-angle
  Transformation Multi-scale GAN
Zhaojie Fang Zhanghao Chen Pengxue Wei Wangting Li Shaochong Zhang Ahmed Elazab Gangyong Jia Ruiquan Ge Changmiao Wang
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.