Automated Optic Nerve Axon Counts in Normal and Glaucomatous Non-Human Primate (NHP) Eyes - Method and Comparison to Semi-Automated Hand Counts

Abstract

 

Purpose:

 

To describe an algorithm and software application (APP) developed for 100% optic nerve (ON) axon counting and to compare its performance to a semi-automated method (SAM) in NHP axon images from normal eyes and experimental glaucoma (EG) eyes with axonal damage ranging from mild to end-stage.

 

Methods:

 

ON cross-sections from 17 normal and 13 EG NHP eyes (25 animals) were imaged at 100X magnification. Calibration (n=500) and validation (n=50) axon image sets ranging from normal to end-stage damage were assembled. Correlation between APP versus SAM axon counts was assessed by Deming regression within the calibration set. A correction factor was then obtained by performing a linear regression of the difference (APP-SAM) versus the average axon counts. This factor was applied to the counts of each validation image and the results were compared to the mean and 95% confidence interval of 5 SAM counts of the validation set performed by a single operator.

 

Results:

 

Calibration set APP counts linearly correlated to SAM counts (APP = 10.59 + 1.028(SAM), R² =0.945, p<0.0001) in normal to end-stage damage images. In the validation set, corrected APP counts fell within the 95% confidence interval of the SAM counts in 42 of the 50 images and were within 12 axons of the confidence intervals in 6 of the 8 remaining images. Axon density maps for both the normal and EG eyes of a representative NHP were then generated.

 

Conclusions:

 

An automated method for ON axon counts has been calibrated and validated relative to SAM counts in normal and EG NHP eyes.  

 

Keywords: optic nerve • imaging/image analysis: non-clinical