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

Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

3.0K
Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
3.0K
Scanning Electron Microscopy01:07

Scanning Electron Microscopy

5.9K
A scanning electron microscope (SEM) is used to study the surface features of a sample by using an electron beam that scans the sample surface in a two-dimensional manner. Typically, areas between ~1 centimeter to 5 micrometers in width can be imaged. SEM can be used to image bacteria, viruses, tissues as well as larger samples like insects. Conventional SEM gives a magnification ranging from 20X to 30,000X and spatial resolution of 50 to 100 nanometers.
Fundamental Principles
Accelerated...
5.9K

You might also read

Related Articles

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

Sort by
Same author

Clearing for insight: A light-sheet microscopy workflow for 3D vascular reconstruction in human first trimester placental villi and decidua.

Placenta·2026
Same author

autoscoRA: Deep Learning to Automate Sharp/van der Heijde Scoring of Radiographic Damage in Rheumatoid Arthritis.

Arthritis & rheumatology (Hoboken, N.J.)·2026
Same author

Machine learning vs. radiomics for discriminating atypical cartilaginous tumors from benign enchondromas on MRI.

Biomedical engineering online·2026
Same author

Pulmonary Artery and Vein Morphology as an Imaging Biomarker for the Diagnosis of Pulmonary Hypertension.

Diagnostics (Basel, Switzerland)·2026
Same author

Minimal age principle versus Bayesian approach to combine age indicators from magnetic resonance imaging for multifactorial forensic age estimation.

Journal of forensic sciences·2026
Same author

Magnetic Properties of Ferritin at Different Levels of Degradation: Implications for MRI-Based Iron Quantification in the Brain.

Magnetic resonance in medicine·2025

Related Experiment Video

Updated: Mar 24, 2026

Serial Block-Face Scanning Electron Microscopy SBF-SEM of Biological Tissue Samples
09:21

Serial Block-Face Scanning Electron Microscopy SBF-SEM of Biological Tissue Samples

Published on: March 26, 2021

8.7K

Optimizing the 3D-reconstruction technique for serial block-face scanning electron microscopy.

Stefan Wernitznig1, Mariella Sele1, Martin Urschler2

  • 1Institute of Cell Biology, Histology and Embryology, Research Unit Electron Microscopic Techniques, Medical University of Graz, Harrachgasse 21, 8010 Graz, Austria.

Journal of Neuroscience Methods
|March 2, 2016
PubMed
Summary
This summary is machine-generated.

A new semi-automatic segmentation software dramatically reduces the time needed for 3D reconstruction of neurons from electron microscopy images, aiding the study of neuronal circuits like the locust

Keywords:
3D-reconstructionLocustSemi-automatic segmentationSerial block-face scanning electron microscopy

More Related Videos

Targeted Studies Using Serial Block Face and Focused Ion Beam Scan Electron Microscopy
09:09

Targeted Studies Using Serial Block Face and Focused Ion Beam Scan Electron Microscopy

Published on: August 10, 2019

9.8K
Three-dimensional Characterization of Interorganelle Contact Sites in Hepatocytes using Serial Section Electron Microscopy
09:12

Three-dimensional Characterization of Interorganelle Contact Sites in Hepatocytes using Serial Section Electron Microscopy

Published on: June 9, 2022

6.5K

Related Experiment Videos

Last Updated: Mar 24, 2026

Serial Block-Face Scanning Electron Microscopy SBF-SEM of Biological Tissue Samples
09:21

Serial Block-Face Scanning Electron Microscopy SBF-SEM of Biological Tissue Samples

Published on: March 26, 2021

8.7K
Targeted Studies Using Serial Block Face and Focused Ion Beam Scan Electron Microscopy
09:09

Targeted Studies Using Serial Block Face and Focused Ion Beam Scan Electron Microscopy

Published on: August 10, 2019

9.8K
Three-dimensional Characterization of Interorganelle Contact Sites in Hepatocytes using Serial Section Electron Microscopy
09:12

Three-dimensional Characterization of Interorganelle Contact Sites in Hepatocytes using Serial Section Electron Microscopy

Published on: June 9, 2022

6.5K

Area of Science:

  • Neuroscience
  • Electron Microscopy
  • Computational Biology

Background:

  • Understanding neuronal circuits requires detailed anatomical and synaptic mapping.
  • Serial block-face scanning electron microscopy (SBEM) generates extensive data for tracing neuronal structures.

Purpose of the Study:

  • To develop and evaluate a novel semi-automatic segmentation algorithm for efficient 3D neuronal reconstruction.
  • To reduce the time-consuming manual process of analyzing SBEM data.

Main Methods:

  • Developed interactive software utilizing image contrast for semi-automatic neuronal segmentation.
  • Algorithm employs a 3D active contour model optimized with SEM image edges.
  • User interaction involves setting initial seed regions for automated segmentation.

Main Results:

  • Achieved significant reduction in 3D reconstruction processing time compared to manual methods.
  • Segmentation results closely matched manual reference segmentations.
  • The algorithm requires no image training and minimal computing power.

Conclusions:

  • The developed semi-automatic segmentation algorithm substantially accelerates the 3D reconstruction of neurons.
  • This advancement facilitates more efficient analysis of complex neuronal architectures, such as the locust LGMD neuron.