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Standardization of a Novel Semi-Automatic Software for Neurite Outgrowth Measurement
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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.

Biorxiv : the Preprint Server for Biology
|June 29, 2026
PubMed
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Automated optic nerve morphometry tools show comparable accuracy for axon counting between classical and deep learning methods. Tool selection depends on species and desired outputs, not a single best performer.

Keywords:
AutomatedComputer VisionDeep LearningMorphometricsOptic nerve

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computational Biology

Background:

  • Automated tools for nerve morphometry are crucial for quantitative analysis in neuroscience research.
  • Existing tools vary in performance, necessitating objective evaluation.
  • Optic nerve morphometry aids in understanding neurological diseases and treatment efficacy.

Purpose of the Study:

  • To systematically review automated nerve morphometry tools.
  • To independently benchmark the performance of these tools on optic nerve datasets.
  • To provide an evidence base for selecting appropriate tools in optic nerve research.

Main Methods:

  • Systematic literature search (PubMed, Embase, Scopus) following PRISMA guidelines.
  • Data extraction on 70 fields including tool capabilities, automation level, and validation.
  • Benchmarking of 18 tools (8 deep learning, 10 classical computer vision) on independent mouse and rat optic nerve datasets with manual annotation as ground truth.

Main Results:

  • Seventy-one studies identified; deep learning tools increased significantly post-2017.
  • Axon counting was the most common output (73%).
  • No single tool outperformed others across both mouse and rat datasets; performance varied significantly. Classical computer vision (CV) and deep learning (DL) methods showed comparable accuracy for axon counting.

Conclusions:

  • No single automated tool consistently excels across all optic nerve datasets.
  • Both classical and deep learning approaches offer comparable accuracy for axon counting.
  • Tool selection should be tailored to specific research needs, including species, tissue preparation, and desired morphometric outputs.