  Goal: In this work, we develop a robust, extensible tool to automatically and
accurately count retinal ganglion cell axons in images of optic nerve tissue
from various animal models of glaucoma. Methods: The U-Net convolutional neural
network architecture was adapted to learn pixelwise axon count density
estimates, which were then integrated over the image area to determine axon
counts. The tool, termed AxoNet, was trained and evaluated using a dataset
containing images of optic nerve regions randomly selected from complete cross
sections of intact rat optic nerves and manually annotated for axon count and
location. Both control and damaged optic nerves were used. This rat-trained
network was then applied to a separate dataset of non-human primate (NHP) optic
nerve images. AxoNet was then compared to two existing automated axon counting
tools, AxonMaster and AxonJ, using both datasets. Results: AxoNet outperformed
the existing tools on both the rat and NHP optic nerve datasets as judged by
mean absolute error, R2 values when regressing automated vs. manual counts, and
Bland-Altman analysis. Conclusion: The proposed tool allows for accurate
quantification of axon numbers as a measure of glaucomatous damage. AxoNet is
robust to variations in optic nerve tissue damage extent, image quality, and
species of mammal. Significance: The deep learning method does not rely on
hand-crafted image features for axon recognition. Therefore, this approach is
not species-specific and can be extended to quantify additional optic nerve
features. It will aid evaluation of optic nerve changes in glaucoma and
potentially other neurodegenerative diseases.
