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
Computed Tomography01:10

Computed Tomography

7.6K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
7.6K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

893
DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
893

You might also read

Related Articles

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

Sort by
Same author

zelll: a fast, framework-free, and flexible implementation of the cell lists algorithm for the Rust programming language.

Bioinformatics advances·2026
Same author

Interface-driven energy-independent charge extraction in GaN photocatalysts.

Nature communications·2026
Same author

Sarcomere analysis in human cardiomyocytes by computing radial frequency spectra.

Biological chemistry·2025
Same author

Using Bayesian priors to overcome non-identifiablility issues in Hidden Markov models.

bioRxiv : the preprint server for biology·2025
Same author

Probing the Dynamics of Yersinia Adhesin A (YadA) in Outer Membranes Hints at Requirements for β-Barrel Membrane Insertion.

Journal of the American Chemical Society·2025
Same author

Bayesian multi-exposure image fusion for robust high dynamic range ptychography.

Optics express·2024

Related Experiment Video

Updated: May 2, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

11.7K

Autonomous reconstruction and segmentation of tomographic data.

Markus Wollgarten1, Michael Habeck2

  • 1Helmholtz Zentrum Berlin für Materialien und Energie, Hahn-Meitner-Platz 1, D-14109 Berlin, Germany.

Micron (Oxford, England : 1993)
|March 12, 2014
PubMed
Summary

This study presents a Bayesian method for analyzing tomographic data, enabling accurate error quantification and segmentation confidence. The approach is validated across diverse experimental conditions for absorption tomography.

Keywords:
SegmentationTomographic reconstructionTomography

More Related Videos

Leveraging Virtual Reality for Immersive Segmentation and Analysis of Cryo-Electron Tomography Data
07:17

Leveraging Virtual Reality for Immersive Segmentation and Analysis of Cryo-Electron Tomography Data

Published on: January 24, 2025

1.6K
Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

9.8K

Related Experiment Videos

Last Updated: May 2, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

11.7K
Leveraging Virtual Reality for Immersive Segmentation and Analysis of Cryo-Electron Tomography Data
07:17

Leveraging Virtual Reality for Immersive Segmentation and Analysis of Cryo-Electron Tomography Data

Published on: January 24, 2025

1.6K
Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

9.8K

Area of Science:

  • Medical Imaging
  • Computational Science
  • Data Analysis

Background:

  • Tomographic data reconstruction and segmentation are crucial for quantitative analysis in various scientific fields.
  • Existing methods may lack robust error estimation and confidence assessment.
  • Absorption tomography presents unique challenges in data processing.

Purpose of the Study:

  • To develop and detail a Bayesian approach for tomographic data reconstruction and segmentation.
  • To enable the quantification of reconstruction errors and segmentation confidence.
  • To evaluate the algorithm's performance under varied experimental settings.

Main Methods:

  • A Bayesian framework was applied to tomographic data reconstruction and segmentation.
  • The methodology was specifically detailed for absorption tomography.
  • The algorithm was tested with varying numbers of projections, incident doses, and material compositions.

Main Results:

  • The Bayesian algorithm successfully reconstructed and segmented tomographic data.
  • Reconstruction errors and segmentation confidence were quantitatively assessed.
  • Performance varied predictably with the number of projections, incident dose, and material properties.

Conclusions:

  • The proposed Bayesian approach provides a reliable method for tomographic data analysis.
  • This technique enhances the accuracy and interpretability of tomographic reconstructions.
  • The findings support the use of this algorithm for quantitative absorption tomography.