AxoNet 2.0: A Deep Learning-Based Tool for Morphometric Analysis of Retinal Ganglion Cell Axons

PURPOSE: Assessment of glaucomatous damage in animal models is facilitated by rapid and accurate quantification of retinal ganglion cell (RGC) axonal loss and morphologic change. However, manual assessment is extremely time- and labor-intensive. Here, we developed AxoNet 2.0, an automated deep learn...

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Detalles Bibliográficos
Autores principales: Goyal, Vidisha, Read, A. Thomas, Ritch, Matthew D., Hannon, Bailey G., Rodriguez, Gabriela Sanchez, Brown, Dillon M., Feola, Andrew J., Hedberg-Buenz, Adam, Cull, Grant A., Reynaud, Juan, Garvin, Mona K., Anderson, Michael G., Burgoyne, Claude F., Ethier, C. Ross
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Association for Research in Vision and Ophthalmology 2023
Materias:
Artificial Intelligence
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10020950/
https://www.ncbi.nlm.nih.gov/pubmed/36917117
http://dx.doi.org/10.1167/tvst.12.3.9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10020950/
https://www.ncbi.nlm.nih.gov/pubmed/36917117
http://dx.doi.org/10.1167/tvst.12.3.9

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