Retinal Image Synthesis for Glaucoma Assessment Using DCGAN and VAE Models

Andres Diaz-Pinto, Adrián Colomer, Valery Naranjo, Sandra Morales, Yanwu Xu, Alejandro F. Frangi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The performance of a Glaucoma assessment system is highly affected by the number of labelled images used during the training stage. However, labelled images are often scarce or costly to obtain. In this paper, we address the problem of synthesising retinal fundus images by training a Variational Autoencoder and an adversarial model on 2357 retinal images. The innovation of this approach is in synthesising retinal images without using previous vessel segmentation from a separate method, which makes this system completely independent. The obtained models are image synthesizers capable of generating any amount of cropped retinal images from a simple normal distribution. Furthermore, more images were used for training than any other work in the literature. Synthetic images were qualitatively evaluated by 10 clinical experts and their consistency were estimated by measuring the proportion of pixels corresponding to the anatomical structures around the optic disc. Moreover, we calculated the mean-squared error between the average 2D-histogram of synthetic and real images, obtaining a small difference of x-4. Further analysis of the latent space and cup size of the images was performed by measuring the Cup/Disc ratio of synthetic images using a state-of-the-art method. The results obtained from this analysis and the qualitative and quantitative evaluation demonstrate that the synthesised images are anatomically consistent and the system is a promising step towards a model capable of generating labelled images.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings
EditorsHujun Yin, Paulo Novais, David Camacho, Antonio J. Tallón-Ballesteros
PublisherSpringer-Verlag Italia
Pages224-232
Number of pages9
ISBN (Print)9783030034924
DOIs
Publication statusPublished - 2018
Event19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018 - Madrid, Spain
Duration: 21 Nov 201823 Nov 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11314 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018
Country/TerritorySpain
CityMadrid
Period21/11/1823/11/18

Keywords

  • DCGAN
  • Fundus images
  • Medical imaging
  • Retinal image synthesis
  • VAE

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