Quantifying the spatial patterns of retinal ganglion cell loss and progression in optic neuropathy by applying a deep learning variational autoencoder approach to optical coherence tomography

• Published in: Frontiers in ophthalmology Vol. 4; p. 1497848
• Main Authors: Wang, Jui-Kai, Johnson, Brett A., Chen, Zhi, Zhang, Honghai, Szanto, David, Woods, Brian, Wall, Michael, Kwon, Young H., Linton, Edward F., Pouw, Andrew, Kupersmith, Mark J., Garvin, Mona K., Kardon, Randy H.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 03.02.2025
• Subjects: glaucoma; non-arteritic anterior ischemic optic neuropathy (NAION); Ophthalmology; optic neuritis (ON); optical coherence tomography (OCT); retinal ganglion cell (RGC) loss; variational autoencoder (VAE); optic neuritis (ON); retinal ganglion cell (RGC) loss; non-arteritic anterior ischemic optic neuropathy (NAION); variational autoencoder (VAE); glaucoma; optical coherence tomography (OCT)

Glaucoma, optic neuritis (ON), and non-arteritic anterior ischemic optic neuropathy (NAION) produce distinct patterns of retinal ganglion cell (RGC) damage. We propose a booster Variational Autoencoder (bVAE) to capture spatial variations in RGC loss and generate latent space (LS) montage maps that...