AxoNet 2.0: A Deep Learning-Based Tool for Morphometric Analysis of Retinal Ganglion Cell Axons

• Type of Material: Article
• Authors: Vidisha Goyal, Georgia Institute of Technology; Thomas A Read, Georgia Institute of Technology; Matthew D Ritch, Georgia Institute of Technology; Bailey G Hannon, Georgia Institute of Technology; Gabriela S Rodriguez, Georgia Institute of Technology; Dillon M Brown, Georgia Institute of Technology; Andrew Feola, Emory University; Adam Hedberg-Buenz, University of Iowa; Grant A Cull, Legacy Research Institute, Portland; Juan Reynaud, Legacy Research Institute, Portland; Mona K Garvin, Legacy Research Institute, Portland; Michael G Anderson, University of Iowa; Claude F Burgoyne, Legacy Research Institute, Portland; Christopher Ethier, Emory University
• Language: English
• Date: 2023-03-01
• Publisher: ASSOC RESEARCH VISION OPHTHALMOLOGY INC
• Publication Version: Final Published Version
• Copyright Statement: 2023 The Authors
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1167/tvst.12.3.9
• Title of Journal or Parent Work: TRANSLATIONAL VISION SCIENCE & TECHNOLOGY
• Volume: 12
• Issue: 3
• Start Page: 9
• End Page: 9
• Grant/Funding Information: Supported by NIH R01 EY025286 (CRE), 5T32 EY007092-32 (BGH), Department of Veteran Affairs R&D Service Career Development Award (RX002342; AJF), NIH NEI EY030871 (AJF), VA EY025580 (MGA), I50 RX003002 (WD, AHB, MGA, MKG), T32DK112751 (AHB), P30 EY025580, I01 RX001481, and the Georgia Research Alliance (CRE).
• Keywords: COUNT; DIAMETER; glaucoma; GLAUCOMA; ELEVATION; PATTERNS; OPTIC-NERVE; RAT MODEL; Ophthalmology; INTRAOCULAR-PRESSURE; deep learning; Science & Technology; Life Sciences & Biomedicine; retinal ganglion cell morphology
• Research Categories: Engineering, Biomedical

Abstract

Purpose: Assessment of glaucomatous damage in animal models is facilitated by rapid and accurate quantification of retinal ganglion cell (RGC) axonal loss and morphologic change. However, manual assessment is extremely time-and labor-intensive. Here, we developed AxoNet 2.0, an automated deep learning (DL) tool that (i) counts normal-appearing RGC axons and (ii) quantifies their morphometry from light micrographs. Methods: A DL algorithm was trained to segment the axoplasm and myelin sheath of normal-appearing axons using manually-annotated rat optic nerve (ON) cross-sectional micrographs. Performance was quantified by various metrics (e.g., soft-Dice coefficient between predicted and ground-truth segmentations). We also quantified axon counts, axon density, and axon size distributions between hypertensive and control eyes and compared to literature reports. Results: AxoNet 2.0 performed very well when compared to manual annotations of rat ON (R2 = 0.92 for automated vs. manual counts, soft-Dice coefficient = 0.81 ± 0.02, mean absolute percentage error in axonal morphometric outcomes < 15%). AxoNet 2.0 also showed promise for generalization, performing well on other animal models (R2 = 0.97 between automated versus manual counts for mice and 0.98 for non-human primates). As expected, the algorithm detected decreased in axon density in hypertensive rat eyes (P ≪ 0.001) with preferential loss of large axons (P < 0.001). Conclusions: AxoNet 2.0 provides a fast and nonsubjective tool to quantify both RGC axon counts and morphological features, thus assisting with assessing axonal damage in animal models of glaucomatous optic neuropathy.