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

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

IntroductionGlaucoma, 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) montag...

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Bibliographic Details
Main Authors: Jui-Kai Wang, Brett A. Johnson, Zhi Chen, Honghai Zhang, David Szanto, Brian Woods, Michael Wall, Young H. Kwon, Edward F. Linton, Andrew Pouw, Mark J. Kupersmith, Mona K. Garvin, Randy H. Kardon
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-02-01
Series:Frontiers in Ophthalmology
Subjects:
variational autoencoder (VAE)
glaucoma
optic neuritis (ON)
non-arteritic anterior ischemic optic neuropathy (NAION)
retinal ganglion cell (RGC) loss
optical coherence tomography (OCT)
Online Access:https://www.frontiersin.org/articles/10.3389/fopht.2024.1497848/full
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https://www.frontiersin.org/articles/10.3389/fopht.2024.1497848/full

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