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Related Experiment Video

Updated: Jul 1, 2025

Visualization of Motor Axon Navigation and Quantification of Axon Arborization In Mouse Embryos Using Light Sheet Fluorescence Microscopy

Visualization of Motor Axon Navigation and Quantification of Axon Arborization In Mouse Embryos Using Light Sheet Fluorescence Microscopy

Published on: May 11, 2018

AxoDetect: an automated nerve image segmentation and quantification workflow for computational nerve modeling.

David A Lloyd¹, Maria Alejandra Gonzalez-Gonzalez^1,2,3, Mario I Romero-Ortega^1,4

¹Departments of Biomedical Engineering and Biomedical Sciences, University of Houston, Houston, TX, United States of America.

Journal of Neural Engineering

|March 8, 2024

Summary

This summary is machine-generated.

We developed AxoDetect, a computer vision tool that rapidly segments nerve structures for bioelectronic medicine. This accelerates the development of neuromodulation therapies by enabling faster in silico model creation for nerve stimulation.

Keywords:

FEM/FEA NEURON autonomic computer vision image segmentation neuromodulation simulation

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Area of Science:

Bioelectronic Medicine
Computational Neuroscience
Histology

Background:

Bioelectronic treatments offer novel therapeutic avenues for chronic and autoimmune diseases by modulating nerve activity.
Optimizing nerve stimulation parameters for treatments, particularly for spleen innervation, is hindered by challenges in analyzing small, complex autonomic nerves.
Current methods for nerve composition analysis rely on time-consuming manual segmentation.

Purpose of the Study:

To develop a fast and efficient software tool for quantifying nerve composition to aid in silico model development.
To create a computer vision algorithm for automated segmentation of nerve structures without prior training.

Main Methods:

Developed AxoDetect, a target- and format-agnostic computer vision software.
AxoDetect segments myelin, endo/epineurium, and myelinated/unmyelinated fibers from nerve images.
The algorithm utilizes tunable pixel threshold filters for flexible tissue detection.

Main Results:

AxoDetect demonstrated an average speed increase of over 10 times compared to existing automatic methods.
Models generated using AxoDetect showed comparable threshold predictions to manual and distribution-based models.
The software accurately identified changes in activation thresholds due to surrounding tissue, such as fat.

Conclusions:

AxoDetect significantly accelerates the design and iteration of neuromodulation models by eliminating the need for training data.
The software's computer vision approach and tunable filters are applicable to various histological analyses.
This tool is expected to advance nerve studies and the design of neural interfaces for bioelectronic therapies.