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 time consuming. Automated approaches have begun to emerge, but shortcomings have limited wide-spread application. 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 semi-supervised 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 performed above an existing baseline automated approach and similarly to two independent experts. Performance of the semi-supervised approach was superior and implemented into AxonDeep.
Conclusion AxonDeep performs automated quantification and segmentation of axons similar to that of experts without the time- and labor-constraints associated with manual performance. The quantitative and objective nature of AxonDeep reduces variability arising from differences in model, methodology, and user that often compromise manual performance of these tasks.
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.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
This report is supported in part by Grants I50 RX003002 (WD, AHB, MGA, MKG); T32DK112751 (AHB); P30 EY025580, I01 RX001481, and R21 EY029991 (MGA)
Disclosure: W. Deng, None; D.A. Soukup, None; A. Hedberg-Buenz, None; M.G. Anderson, None, M.K. Garvin, None.