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

Journal: Translational vision science & technology
PMID:

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.

Authors

  • Wenxiang Deng
    Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA.
  • Adam Hedberg-Buenz
    Iowa City VA Center for the Prevention and Treatment of Visual Loss, Iowa City VA Health Care System, Iowa City, IA, USA.
  • Dana A Soukup
    Iowa City VA Center for the Prevention and Treatment of Visual Loss, Iowa City VA Health Care System, Iowa City, IA, USA.
  • Sima Taghizadeh
    Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA.
  • Kai Wang
    Department of Rheumatology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China.
  • Michael G Anderson
    Iowa City VA Center for the Prevention and Treatment of Visual Loss, Iowa City VA Health Care System, Iowa City, IA, USA.
  • Mona K Garvin
    Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, 52242, United States; Iowa City VA Health Care System, Iowa City, IA, 52246, United States. Electronic address: mona-garvin@uiowa.edu.