AxonDeep: Automated Optic Nerve Axon Segmentation in Mice With Deep Learning.

ABSTRACT:

Purpose

Optic nerve damage is the principal feature of glaucoma and contributes to vision loss in many diseases. In animal models, nerve health has traditionally been assessed by human experts that grade damage qualitatively or manually quantify axons from sampling limited areas from histologic cross sections of nerve. Both approaches are prone to variability and are time consuming. First-generation automated approaches have begun to emerge, but all have significant shortcomings. Here, we seek improvements through use of deep-learning approaches for segmenting and quantifying axons from cross-sections of mouse optic nerve.

Methods

Two deep-learning approaches were developed and evaluated: (1) a traditional supervised approach using a fully convolutional network trained with

SUBMITTER: Deng W 

PROVIDER: S-EPMC8709929 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature