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

2.0K
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...
2.0K
Three-Dimensional Microscopy in Microbiology01:28

Three-Dimensional Microscopy in Microbiology

907
Three-dimensional imaging techniques are essential in cell biology, allowing researchers to visualize intricate cellular structures with high resolution. Two prominent methods, Differential Interference Contrast Microscopy (DIC) and Confocal Scanning Laser Microscopy (CSLM), provide distinct advantages for imaging live and thick specimens, respectively.Differential Interference Contrast MicroscopyDIC microscopy enhances contrast in transparent, unstained samples by converting phase...
907

You might also read

Related Articles

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

Sort by
Same author

Boosting Photocatalytic Water Splitting of Polymeric C<sub>60</sub> by Reduced Dimensionality from Two-Dimensional Monolayer to One-Dimensional Chain.

The journal of physical chemistry letters·2023
Same author

Accuracy of Wireless Pulse Oximeter on Preterm or <2.5 kg Infants.

American journal of perinatology·2023
Same author

Chromogenic Photonic Crystal Sensors Enabled by Multistimuli-Responsive Shape Memory Polymers.

Small (Weinheim an der Bergstrasse, Germany)·2018
Same author

Image Segmentation Using Hierarchical Merge Tree.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2016
Same author

Crowdsourcing the creation of image segmentation algorithms for connectomics.

Frontiers in neuroanatomy·2015
Same author

Semantic Image Segmentation with Contextual Hierarchical Models.

IEEE transactions on pattern analysis and machine intelligence·2015

Related Experiment Video

Updated: May 3, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

24.9K

A modular hierarchical approach to 3D electron microscopy image segmentation.

Ting Liu1, Cory Jones2, Mojtaba Seyedhosseini2

  • 1Scientific Computing and Imaging Institute, University of Utah, United States; School of Computing, University of Utah, United States.

Journal of Neuroscience Methods
|February 5, 2014
PubMed
Summary

This study presents an automated method for reconstructing neural circuits from electron microscopy images. The approach achieves near-human accuracy in neuron segmentation and state-of-the-art reconstruction, advancing connectomics research.

Keywords:
Electron microscopyHierarchical segmentationImage segmentationNeuron reconstructionSemi-automatic segmentation

More Related Videos

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes
11:19

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

Published on: March 20, 2018

12.6K
Correlative Confocal and 3D Electron Microscopy of a Specific Sensory Cell
08:00

Correlative Confocal and 3D Electron Microscopy of a Specific Sensory Cell

Published on: July 19, 2015

12.8K

Related Experiment Videos

Last Updated: May 3, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

24.9K
Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes
11:19

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

Published on: March 20, 2018

12.6K
Correlative Confocal and 3D Electron Microscopy of a Specific Sensory Cell
08:00

Correlative Confocal and 3D Electron Microscopy of a Specific Sensory Cell

Published on: July 19, 2015

12.8K

Area of Science:

  • Neuroscience
  • Computational Biology
  • Image Analysis

Background:

  • Connectomics, the study of neural circuits, is a complex challenge in neuroscience.
  • Electron microscopy (EM) image analysis is crucial for connectomics research.
  • Automated and semi-automated methods can significantly aid EM image analysis.

Purpose of the Study:

  • To develop a fully automatic approach for intra-section segmentation and inter-section reconstruction of neurons using EM images.
  • To introduce a semi-automatic method for improved intra-segmentation with minimal user intervention.

Main Methods:

  • A hierarchical merge tree structure is employed for region hypothesis representation.
  • Supervised classification techniques evaluate region potentials for intra-section segmentation.
  • Consistency constraints are used to resolve the merge tree for final segmentation.
  • Supervised learning-based linking procedure is utilized for inter-section neuron reconstruction.

Main Results:

  • The automatic method achieves close-to-human intra-segmentation accuracy.
  • The method demonstrates state-of-the-art inter-section reconstruction accuracy.
  • The semi-automatic method further enhances intra-segmentation accuracy.

Conclusions:

  • The proposed fully automatic method effectively performs neuron segmentation and reconstruction from EM images.
  • The semi-automatic method offers an efficient alternative for improved segmentation with user guidance.
  • These advancements contribute to the progress of connectomics research.