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A Systematic Review and Independent Benchmarking of Automated Nerve Morphometry Methods
Benton Chuter1, Min Young Kim1, Andrew B Stiemke2
1Department of Ophthalmology, Hamilton Eye Institute, The University of Tennessee Health Science Center, Memphis, TN 38163, USA.
Objective:
To systematically review automated nerve morphometry tools and independently benchmark their performance on independent optic nerve datasets.
Design:
Systematic review and comparative benchmarking study.
Controls:
Benchmarking was performed using paraphenylenediamine-stained mouse (n = 85) and rat (n = 44) optic nerve images with manually annotated axon counts as ground truth.
Methods:
Published studies describing automated or semi-automated neural tissue morphometry tools were identified through systematic searches of PubMed, Embase, and Scopus through January 2026 following PRISMA guidelines. Data extraction covered 70 fields across tool capabilities, imaging modality, species, automation level, and validation approach. Eighteen eligible tools (8 deep learning [DL], 10 classical computer vision [CV]) were benchmarked on both mouse and rat independent datasets.
Main Outcome Measures:
Performance was assessed by mean absolute percentage error (MAPE), Pearson correlation, and median predicted-to-ground-truth ratio. Tools were ranked per image and compared using Friedman tests with Nemenyi post-hoc analysis.
Results:
Seventy-one studies met inclusion criteria, spanning from 1999 to 2026. Deep learning methods represented 38% (27/71) of studies, increasing from 0% before 2017 to over 55% of publications after 2020. Axon counting was the most common output (73%, 52/71), while only 35% (25/71) reported g-ratio. Among benchmarked tools, Marina (CV, 2010) achieved the lowest average MAPE (32.9%). The top five tools (MAPE ranging from 32.9 to 44.8%) included both CV and DL methods and were statistically indistinguishable by Friedman-Nemenyi analysis (p > 0.05). Performance varied substantially across datasets: AxonJ (CV) achieved the second best MAPE on rat images (27.7%) but the worst on mouse images (438.6%).
Conclusions:
No single tool demonstrated consistently superior performance across both datasets. Classical and deep learning approaches achieved comparable accuracy for axon counting. Tool selection should be guided by target species, tissue preparation protocol, and desired morphometric outputs. This systematic review and independent benchmarking study provide an evidence base for tool selection in optic nerve research.
