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

Ligand-mediated suppression of Ostwald ripening enables low-temperature sol-gel ZnO for efficient inverted flexible organic photovoltaics.

Nature communications·2026
Same author

Correction to "Synergistic S-O Coordination in Subnanometer Indium Oxysulfide Coils for CO<sub>2</sub> Photoreduction".

Inorganic chemistry·2026
Same author

Explainable Knowledge-Guided Algorithm for Contrast Extravasation Detection on Computed Tomography.

IEEE journal of translational engineering in health and medicine·2026
Same author

Pan-cancer Distant Metastasis Prediction Based on Graph Neural Network.

Interdisciplinary sciences, computational life sciences·2026
Same author

Synergistic S-O Coordination in Subnanometer Indium Oxysulfide Coils for CO<sub>2</sub> Photoreduction.

Inorganic chemistry·2026
Same author

Lactate-associated transcriptomic alterations and malignant phenotypes in colorectal cancer.

Discover oncology·2026

Related Experiment Video

Updated: May 14, 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

Dendritic spines detection based on directional morphological filter and shortest path.

Ran Su1, Changming Sun, Tuan D Pham

  • 1School of Engineering and Information Technology, The University of New South Wales, Canberra, ACT 2600, Australia.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for detecting small, varied dendritic spines in neuron images. The algorithm effectively extracts dendrite structures and segments spines, achieving good results in image analysis.

More Related Videos

Imaging Dendritic Spines of Rat Primary Hippocampal Neurons using Structured Illumination Microscopy
14:11

Imaging Dendritic Spines of Rat Primary Hippocampal Neurons using Structured Illumination Microscopy

Published on: May 4, 2014

Three-dimensional Quantification of Dendritic Spines from Pyramidal Neurons Derived from Human Induced Pluripotent Stem Cells
10:18

Three-dimensional Quantification of Dendritic Spines from Pyramidal Neurons Derived from Human Induced Pluripotent Stem Cells

Published on: October 10, 2015

Related Experiment Videos

Last Updated: May 14, 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

Imaging Dendritic Spines of Rat Primary Hippocampal Neurons using Structured Illumination Microscopy
14:11

Imaging Dendritic Spines of Rat Primary Hippocampal Neurons using Structured Illumination Microscopy

Published on: May 4, 2014

Three-dimensional Quantification of Dendritic Spines from Pyramidal Neurons Derived from Human Induced Pluripotent Stem Cells
10:18

Three-dimensional Quantification of Dendritic Spines from Pyramidal Neurons Derived from Human Induced Pluripotent Stem Cells

Published on: October 10, 2015

Area of Science:

  • Neuroscience
  • Image Analysis
  • Computational Biology

Background:

  • Dendritic spines are crucial neuronal structures involved in synaptic plasticity.
  • Their small size and diverse morphology present significant challenges for automated detection in microscopy images.
  • Accurate identification of dendritic spines is vital for understanding neural function and disease.

Purpose of the Study:

  • To develop and present a novel algorithm for the automated detection of dendritic spines in 2D neuron images.
  • To improve the accuracy and efficiency of spine segmentation compared to existing methods.

Main Methods:

  • A new iterative method for dendrite backbone (centerline) extraction using smoothing and directional morphological filtering.
  • Improved Hessian filtering for refining dendrite structure extraction.
  • A shortest path algorithm to delineate dendrite boundaries.
  • Segmentation of spines from regions outside the extracted dendrite boundaries.

Main Results:

  • The proposed method successfully extracts dendrite backbones and boundaries.
  • Spines are effectively segmented from the surrounding dendrite structures.
  • The algorithm demonstrated robust performance across various test images, achieving satisfactory detection results.

Conclusions:

  • The developed algorithm offers a promising approach for accurate dendritic spine detection in 2D neuron images.
  • This method can aid researchers in quantitative analysis of neuronal morphology and synaptic function.
  • Further validation on diverse datasets could enhance its applicability in neuroscience research.