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An Open-Source, User-Friendly Machine-Learning Method for Automated Segmentation and Analysis of Peripheral Nerve

Marissa Suchyta1, Beth Dohrmann1, Samir Mardini1

  • 1From the Division of Plastic Surgery, Mayo Clinic.

Plastic and Reconstructive Surgery
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Summary
This summary is machine-generated.

This study presents an accessible, open-source method for peripheral nerve analysis, significantly reducing time and bias in quantifying myelin and axonal areas for nerve regeneration research.

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Area of Science:

  • Neuroscience
  • Histology
  • Biomedical Engineering

Background:

  • Quantitative neuromorphometry is crucial for nerve regeneration research.
  • Manual histological analysis is time-consuming, prone to error, and biased.
  • Accurate segmentation of myelin and axonal areas is essential.

Purpose of the Study:

  • To develop and validate a user-friendly, open-source method for peripheral nerve cross-section analysis.
  • To reduce time and bias in histological analysis.
  • To enable whole-nerve analysis instead of random sampling.

Main Methods:

  • Rat facial nerve segments were processed and stained.
  • Whole nerve cross-sections were scanned and pre-processed in ImageJ.
  • Open-source software (Ilastik, CellProfiler) was used for segmentation and quantification of myelin and axons.

Main Results:

  • The novel protocol accurately quantifies axon counts and g-ratio, comparable to manual methods.
  • Analysis of entire nerve cross-sections is achievable in under 5 minutes.
  • High reliability was demonstrated through Bland-Altman plots and intraclass correlation coefficients.

Conclusions:

  • The presented protocol offers a novel, accurate, and accessible method for analyzing histological nerve cross-sections.
  • This open-source approach significantly decreases user time and potential bias.
  • It facilitates comprehensive analysis of entire nerves, advancing nerve regeneration research.