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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
|January 22, 2025
Summary
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.
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.
