Fundus2Angio: A Conditional GAN Architecture for Generating Fluorescein
Angiography Images from Retinal Fundus Photography
- URL: http://arxiv.org/abs/2005.05267v2
- Date: Tue, 29 Sep 2020 08:46:05 GMT
- Title: Fundus2Angio: A Conditional GAN Architecture for Generating Fluorescein
Angiography Images from Retinal Fundus Photography
- Authors: Sharif Amit Kamran, Khondker Fariha Hossain, Alireza Tavakkoli,
Stewart Lee Zuckerbrod, Salah A. Baker, Kenton M. Sanders
- Abstract summary: There are no non-invasive systems capable of generating Fluorescein Angiography images.
Fundus photography is a non-invasive imaging technique that can be completed in a few seconds.
We propose a conditional generative adversarial network (GAN) to translate fundus images to FA images.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Carrying out clinical diagnosis of retinal vascular degeneration using
Fluorescein Angiography (FA) is a time consuming process and can pose
significant adverse effects on the patient. Angiography requires insertion of a
dye that may cause severe adverse effects and can even be fatal. Currently,
there are no non-invasive systems capable of generating Fluorescein Angiography
images. However, retinal fundus photography is a non-invasive imaging technique
that can be completed in a few seconds. In order to eliminate the need for FA,
we propose a conditional generative adversarial network (GAN) to translate
fundus images to FA images. The proposed GAN consists of a novel residual block
capable of generating high quality FA images. These images are important tools
in the differential diagnosis of retinal diseases without the need for invasive
procedure with possible side effects. Our experiments show that the proposed
architecture outperforms other state-of-the-art generative networks.
Furthermore, our proposed model achieves better qualitative results
indistinguishable from real angiograms.
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