1
|
Deng W, Hedberg-Buenz A, Soukup DA, Taghizadeh S, Wang K, Anderson MG, Garvin MK. AxonDeep: Automated Optic Nerve Axon Segmentation in Mice With Deep Learning. Transl Vis Sci Technol 2021; 10:22. [PMID: 34932117 PMCID: PMC8709929 DOI: 10.1167/tvst.10.14.22] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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 only labeled data and (2) a semisupervised approach trained with both labeled and unlabeled data using a generative-adversarial-network framework. Results 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. Conclusions 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. Translational Relevance Use of deep learning for axon quantification provides rapid, objective, and higher throughput analysis of optic nerve that would otherwise not be possible.
Collapse
Affiliation(s)
- Wenxiang Deng
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA.,Iowa City VA Center for the Prevention and Treatment of Visual Loss, Iowa City VA Health Care System, 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.,Department of Molecular Physiology and Biophysics, The University of Iowa, 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.,Department of Molecular Physiology and Biophysics, The University of Iowa, Iowa City, IA, USA
| | - Sima Taghizadeh
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA
| | - Kai Wang
- Department of Biostatistics, The University of Iowa, Iowa City, IA, USA
| | - 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.,Department of Molecular Physiology and Biophysics, The University of Iowa, Iowa City, IA, USA.,Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA, USA
| | - Mona K Garvin
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA.,Iowa City VA Center for the Prevention and Treatment of Visual Loss, Iowa City VA Health Care System, Iowa City, IA, USA
| |
Collapse
|