Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Patterns of Muscle Health in Single- and Multi-Site Chronic Pain: A UK Biobank Normative Modeling Study.

medRxiv : the preprint server for health sciences·2026
Same author

Optimization in Sparse 2D to Dense 3D Weakly Supervised Learning: Application to Multi-Label Segmentation of Large ex vivo MRI Data.

ArXiv·2026
Same author

Charting Cervical Spinal Cord Morphometry Across the Lifespan.

bioRxiv : the preprint server for biology·2026
Same author

Spinal cord imaging for multiple sclerosis: Advances, priorities, and opportunities.

Multiple sclerosis (Houndmills, Basingstoke, England)·2026
Same author

Automatic multiple sclerosis lesion segmentation in the spinal cord using 3 T and 7 T MP2RAGE images.

Multiple sclerosis and related disorders·2026
Same author

Segmentation of spinal rootlets across MRI contrasts with RootletSeg.

Scientific reports·2026

Related Experiment Video

Updated: Sep 27, 2025

Author Spotlight: Unveiling the Molecular Basis of Pain Perception and Neuropathic Pain
05:28

Author Spotlight: Unveiling the Molecular Basis of Pain Perception and Neuropathic Pain

Published on: August 9, 2024

1.3K

Rapid, automated nerve histomorphometry through open-source artificial intelligence.

Simeon Christian Daeschler1, Marie-Hélène Bourget2, Dorsa Derakhshan3

  • 1SickKids Research Institute, Neuroscience and Mental Health Program, Hospital for Sick Children (SickKids), Toronto, ON, Canada. simeondaeschler@gmail.com.

Scientific Reports
|April 9, 2022
PubMed
Summary
This summary is machine-generated.

A new deep learning model automates the analysis of peripheral nerve fibers in microscopic images. This tool significantly speeds up histomorphometry, offering accurate measurements for biomedical research.

More Related Videos

Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales
11:41

Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales

Published on: November 14, 2010

33.8K
Semi-Quantitative Determination of Dopaminergic Neuron Density in the Substantia Nigra of Rodent Models using Automated Image Analysis
06:09

Semi-Quantitative Determination of Dopaminergic Neuron Density in the Substantia Nigra of Rodent Models using Automated Image Analysis

Published on: February 2, 2021

4.6K

Related Experiment Videos

Last Updated: Sep 27, 2025

Author Spotlight: Unveiling the Molecular Basis of Pain Perception and Neuropathic Pain
05:28

Author Spotlight: Unveiling the Molecular Basis of Pain Perception and Neuropathic Pain

Published on: August 9, 2024

1.3K
Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales
11:41

Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales

Published on: November 14, 2010

33.8K
Semi-Quantitative Determination of Dopaminergic Neuron Density in the Substantia Nigra of Rodent Models using Automated Image Analysis
06:09

Semi-Quantitative Determination of Dopaminergic Neuron Density in the Substantia Nigra of Rodent Models using Automated Image Analysis

Published on: February 2, 2021

4.6K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computational Biology

Background:

  • Accurate histomorphometry of peripheral nerves is crucial for understanding nerve regeneration and disease.
  • Manual analysis of nerve fiber morphology is time-consuming and prone to variability.

Purpose of the Study:

  • To develop and validate a deep learning model for automated segmentation and histomorphometry of myelinated peripheral nerve fibers.
  • To assess the accuracy and efficiency of the automated model compared to manual methods.

Main Methods:

  • A convolutional neural network within the AxonDeepSeg framework was trained on light microscopic images of rat nerves.
  • Automated segmentation of axons and myelin sheaths was performed.
  • Morphometric parameters (axon diameter, myelin thickness, g-ratio) were automatically extracted and compared to manual measurements.

Main Results:

  • The model achieved high accuracy in segmenting axons (0.93 overlap) and myelin sheaths (0.99 overlap).
  • Nerve fibers were identified with high sensitivity (0.99) and precision (0.97).
  • Automated histomorphometry demonstrated superior agreement with manual analysis and reduced analysis time to less than 2.5% of manual methods.

Conclusions:

  • The open-source deep learning model enables rapid and accurate morphometry of peripheral nerve cross-sections.
  • This tool offers significant time savings and facilitates the extraction of objective morphologic data from large image datasets in biomedical research.