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

Neuron Structure01:30

Neuron Structure

Neurons are the main type of cell in the nervous system that generate and transmit electrochemical signals. They primarily communicate with each other using neurotransmitters at specific junctions called synapses. Neurons come in many shapes that often relate to their function, but most share three main structures: an axon and dendrites that extend out from a cell body.
Structure and Function of Neurons
The neuronal cell body—the soma— houses the nucleus and organelles vital to cellular...
Neuron Structure01:31

Neuron Structure

Overview

You might also read

Related Articles

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

Sort by
Same author

Modeling 3D mesoscaled neuronal complexity through learning-based dynamic morphometric convolution.

Brain informatics·2026
Same author

AISleep: Automated and interpretable sleep staging from single-channel EEG data.

Patterns (New York, N.Y.)·2025
Same author

Bridging the dimensional gap from planar spatial transcriptomics to 3D cell atlases.

Nature methods·2025
Same author

A mouse brain atlas based on dendritic microenvironments.

Nature neuroscience·2025
Same author

Author Correction: Confocal Airy beam oblique light-sheet tomography for brain-wide cell type distribution and morphology.

Nature methods·2025
Same author

Confocal Airy beam oblique light-sheet tomography for brain-wide cell type distribution and morphology.

Nature methods·2025

Related Experiment Video

Updated: Jun 12, 2026

Dendritic Spine Quantification Using an Automatic Three-Dimensional Neuron Reconstruction Software
07:45

Dendritic Spine Quantification Using an Automatic Three-Dimensional Neuron Reconstruction Software

Published on: September 27, 2024

Automatic reconstruction of 3D neuron structures using a graph-augmented deformable model.

Hanchuan Peng1, Zongcai Ruan, Deniz Atasoy

  • 1Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA. pengh@janelia.hhmi.org

Bioinformatics (Oxford, England)
|June 10, 2010
PubMed
Summary

We developed a graph-augmented deformable model (GD) to accurately reconstruct 3D neuron structures from challenging microscopic images. This method improves tracing accuracy and robustness for brain research.

More Related Videos

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
12:49

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Related Experiment Videos

Last Updated: Jun 12, 2026

Dendritic Spine Quantification Using an Automatic Three-Dimensional Neuron Reconstruction Software
07:45

Dendritic Spine Quantification Using an Automatic Three-Dimensional Neuron Reconstruction Software

Published on: September 27, 2024

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
12:49

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

  • Neuroscience
  • Computational Biology
  • Image Analysis

Background:

  • Accurate 3D neuron reconstruction is crucial for understanding brain wiring and function.
  • Existing methods struggle with low signal-to-noise ratio and discontinuous neurites in 3D microscopy images.

Purpose of the Study:

  • To develop a robust method for tracing 3D neuronal structures, even with broken or fuzzy patterns.
  • To improve the accuracy and efficiency of digital neuron reconstruction.

Main Methods:

  • Developed a graph-augmented deformable model (GD) using a variational approach with geodesic shortest paths.
  • Employed a two-step solution: shortest path graph algorithm followed by discrete deformable curve optimization.
  • Incorporated optional user-defined prior curves for domain knowledge integration.

Main Results:

  • The GD method demonstrated superior reconstruction accuracy, consistency, robustness, and speed compared to existing methods.
  • Successfully applied GD to challenging 3D neuronal datasets from fruit fly, C. elegans, and mouse.
  • Utilized GD for cataloging fruit fly neurite morphology and estimating synaptic bouton density in mouse brains.

Conclusions:

  • The graph-augmented deformable model (GD) provides a powerful and versatile tool for 3D neuron reconstruction.
  • GD enhances the ability to analyze neuronal morphology and connectivity in complex biological datasets.
  • The software is freely available as part of the V3D-Neuron 1.0 package.