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Related Experiment Video

Updated: Nov 21, 2025

Author Spotlight: Efficient Retinal Ganglion Cell Counting in Mouse Models of Glaucoma for Treatment Evaluation
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Author Spotlight: Efficient Retinal Ganglion Cell Counting in Mouse Models of Glaucoma for Treatment Evaluation

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A novel retinal ganglion cell quantification tool based on deep learning.

Luca Masin1, Marie Claes1, Steven Bergmans1

  • 1Department of Biology, Neural Circuit Development and Regeneration Research Group, KU Leuven, Leuven, Belgium.

Scientific Reports
|January 13, 2021
PubMed
Summary

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This summary is machine-generated.

A new deep learning tool, RGCode, fully automates retinal ganglion cell (RGC) quantification in mice. This software accurately counts RGCs, aiding glaucoma research and the development of neuroprotective therapies.

Area of Science:

  • Ophthalmology
  • Neuroscience
  • Computational Biology

Background:

  • Glaucoma is a leading cause of blindness due to retinal ganglion cell (RGC) loss.
  • Understanding glaucoma pathogenesis and developing therapies requires accurate RGC survival quantification.
  • Rodent models are crucial preclinical tools for glaucoma research.

Purpose of the Study:

  • To develop a novel, fully automated deep learning pipeline for quantifying RGCs in the entire murine retina.
  • To provide a user-friendly software tool (RGCode) for accurate RGC counting and analysis.
  • To assess the performance and applicability of RGCode in glaucoma research.

Main Methods:

  • Development of a deep learning pipeline (RGCode) for automated RGC quantification.
  • Training the model on RBPMS-immunostained healthy and glaucomatous murine retinas.

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  • Validation of RGCode against manual RGC counts and testing its adaptability to FluoroGold-traced RGCs.

    • Main Results:

    • RGCode enables fully automated quantification of RGCs, retinal area, and density in murine retinas.
    • The software provides output images with computed counts and isodensity maps.
    • RGCode demonstrated excellent performance compared to manual quantification and showed potential for broader application.

    Conclusions:

    • RGCode offers a reliable and efficient tool for RGC quantification in preclinical glaucoma research.
    • The automated pipeline facilitates the study of RGC survival and the development of neuroprotective strategies.
    • The adaptability of RGCode to different staining methods enhances its utility in vision research.