Application of conditional GAN models in optic disc/optic cup segmentation of retinal fundus images

Tales H. Carvalho; Carlos H. V. Moraes; Rafael C. Almeida; Danilo H. Spadoti

doi:10.1117/12.2606209

10 December 2021 Application of conditional GAN models in optic disc/optic cup segmentation of retinal fundus images

Tales H. Carvalho, Carlos H. V. Moraes, Rafael C. Almeida, Danilo H. Spadoti

Author Affiliations +

Proceedings Volume 12088, 17th International Symposium on Medical Information Processing and Analysis; 1208818 (2021) https://doi.org/10.1117/12.2606209
Event: Seventeenth International Symposium on Medical Information Processing and Analysis, 2021, Campinas, Brazil

Abstract

Analysis of retinal fundus images have been proven to provide relevant information about the diagnoses of several pathologies. Among them, glaucoma stands out as an important pathology due to the need for early treatment. Moreover, the relationship between optic disc and optic cup regions provided by retinal fundus image analysis can aid in diagnosis. Automatically generating such a relation is, therefore, an important feature for ensuring quicker and more precise conclusions. This paper evaluates the use of Conditional GAN (Generative Adversarial Networks) for an optic disc and optic cup segmentation task. Conditional GANs are hybrid machine learning models that are able to generate data based on conditioned training. The results demonstrate that the addressed method generates valid segmentation images for optic disc and optic cup location, with approximately 95% and 85% accuracy, respectively

Citation Download Citation

Tales H. Carvalho, Carlos H. V. Moraes, Rafael C. Almeida, and Danilo H. Spadoti "Application of conditional GAN models in optic disc/optic cup segmentation of retinal fundus images", Proc. SPIE 12088, 17th International Symposium on Medical Information Processing and Analysis, 1208818 (10 December 2021); https://doi.org/10.1117/12.2606209

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE