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AxonDeep: Automated Optic Nerve Axon Segmentation in Mice With Deep Learning
Journal article   Open access   Peer reviewed

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

Wenxiang Deng, Adam Hedberg-Buenz, Dana A Soukup, Sima Taghizadeh, Kai Wang, Michael G Anderson and Mona K Garvin
Translational vision science & technology, Vol.10(14), 22
12/01/2021
DOI: 10.1167/tvst.10.14.22
PMCID: PMC8709929
PMID: 34932117
url
https://doi.org/10.1167/tvst.10.14.22View
Published (Version of record) Open Access

Abstract

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. Two deep-learning approaches were developed and evaluated: (1) a traditional supervised approach using a fully convolutional network trained with only labeled data and (2) a semisupervised approach trained with both labeled and unlabeled data using a generative-adversarial-network framework. From comparisons with an independent test set of images with manually marked axon centers and boundaries, both deep-learning approaches outperformed an existing baseline automated approach and similarly to two independent experts. Performance of the semisupervised approach was superior and implemented into AxonDeep. AxonDeep performs automated quantification and segmentation of axons from healthy-appearing nerves and those with mild to moderate degrees of damage, similar to that of experts without the variability and constraints associated with manual performance. Use of deep learning for axon quantification provides rapid, objective, and higher throughput analysis of optic nerve that would otherwise not be possible.
 Animals Axons Deep Learning Glaucoma - diagnosis Mice Optic Nerve - diagnostic imaging Optic Nerve Injuries

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