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

Journal: Translational vision science & technology
PMID: 36917117

Abstract

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 learning (DL) tool that (i) counts normal-appearing RGC axons and (ii) quantifies their morphometry from light micrographs.

Authors

  • Vidisha Goyal
    School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
  • A Thomas Read
    Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States.
  • Matthew D Ritch
    Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States.
  • Bailey G Hannon
    George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States.
  • Gabriela Sanchez Rodriguez
    Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
  • Dillon M Brown
    Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
  • Andrew J Feola
    Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States.
  • Adam Hedberg-Buenz
    Iowa City VA Center for the Prevention and Treatment of Visual Loss, Iowa City VA Health Care System, Iowa City, IA, USA.
  • Grant A Cull
    Devers Eye Institute, Legacy Research Institute, Portland, Oregon, United States.
  • Juan Reynaud
    Devers Eye Institute, Legacy Research Institute, Portland, Oregon, United States.
  • Mona K Garvin
    Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, 52242, United States; Iowa City VA Health Care System, Iowa City, IA, 52246, United States. Electronic address: mona-garvin@uiowa.edu.
  • Michael G Anderson
    Iowa City VA Center for the Prevention and Treatment of Visual Loss, Iowa City VA Health Care System, Iowa City, IA, USA.
  • Claude F Burgoyne
    Devers Eye Institute, Legacy Research Institute, Portland, Oregon, United States.
  • C Ross Ethier
    Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States. ross.ethier@bme.gatech.edu.

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