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Related Experiment Video

Updated: May 5, 2026

Stereo-Imaging System DLT Calibration to Capture 3D In Situ Displacements of Stretched Peripheral Nerves

Stereo-Imaging System DLT Calibration to Capture 3D In Situ Displacements of Stretched Peripheral Nerves

Published on: January 12, 2024

A new computerized morphometric analysis for peripheral nerve study.

Chase A Tobin¹, Ziyi Wang¹, Lin-Ling Zhang¹

¹Department of Plastic Surgery, Medical College of Wisconsin, Milwaukee, Wisconsin.

Journal of Reconstructive Microsurgery

|December 11, 2013

Summary

This summary is machine-generated.

A new automated ImageJ method accurately quantifies axon numbers and mean axon area in rat sciatic nerves. This approach offers improved efficiency and reduced operator bias compared to manual or semiautomated techniques.

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Area of Science:

Neuroscience
Morphometry
Image Analysis

Background:

Manual and semiautomated methods are commonly used for quantifying axon numbers and mean axon area.
These traditional methods can be time-consuming and prone to operator error and bias.

Purpose of the Study:

To introduce and validate a novel, fully automated method for morphometric analysis of nerve axons.
To compare the accuracy and efficiency of the new ImageJ-based method against established manual and semiautomated techniques.

Main Methods:

A fully automated morphometric analysis was developed using ImageJ and Paint.net software.
Six rat sciatic nerves were analyzed for axon count and mean axon area using the new ImageJ method, manual counting, and the semiautomated MetaVue method.

Main Results:

Axon counts showed no statistically significant difference between manual counting and the ImageJ method (p > 0.5).
The mean axon area measurements differed significantly between MetaVue and ImageJ (p < 0.01), with ImageJ demonstrating lower standard error (0.32 vs. 1.07) and coefficient of variation (0.087 vs. 0.195).

Conclusions:

The ImageJ-based automated method provides a convenient, time-efficient, and accurate alternative for quantifying axon morphometry.
This automated approach minimizes operator error and bias, enhancing the reliability of morphometric analyses.