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AxonQuantifier: A semi-automated program for quantifying axonal density from whole-mounted optic nerves.

Micalla G Peng¹, Jonathan Lee¹, Wilson Ho¹

¹Department of Ophthalmology, Keck School of Medicine, USC Roski Eye Institute, University of Southern California, Los Angeles, CA, USA.

Journal of Neuroscience Methods

|June 14, 2023

Summary

A new semi-automated method, AxonQuantifier, efficiently quantifies retinal ganglion cell (RGC) axon density in whole optic nerves. This tool improves accuracy and speed compared to manual RGC axon density measurements.

Keywords:

Axon Neuro-regeneration Neuroprotection Optic nerve Quantification Retinal ganglion cell

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Area of Science:

Neuroscience
Ophthalmology
Biomedical Imaging

Background:

Retinal ganglion cell (RGC) axon density quantification is crucial for understanding optic nerve injury and regeneration.
Existing manual methods are time-consuming and prone to inter-rater variability.

Purpose of the Study:

To present and validate AxonQuantifier, a semi-automated method for quantifying RGC axon density.
To compare the efficiency and accuracy of AxonQuantifier against manual quantification.

Main Methods:

Developed a semi-automated algorithm, AxonQuantifier, operating on ImageJ software.
Used longitudinal confocal microscopy images of whole-mounted rat optic nerves post-injury.
Quantified RGC axon density at various distances distal to the optic nerve crush site.

Main Results:

AxonQuantifier demonstrated a 4-fold increase in time efficiency compared to manual quantification.
The semi-automated method showed improved inter-rater agreement and reduced bias.
AxonQuantifier tended to slightly underestimate axon density relative to manual assessment.

Conclusions:

AxonQuantifier is a reliable and efficient tool for assessing RGC axon density in whole-mount optic nerve preparations.
This method offers a significant advancement over manual quantification techniques.