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

2.5K
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...
2.5K

You might also read

Related Articles

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

Sort by
Same author

Pseudoephedrine Improves Chronic Obstructive Pulmonary Disease by Regulating Airway Senescence and Mitochondrial Function through PI3K/AKT/mTOR Axis.

Biomolecules & therapeutics·2026
Same author

Integrating probabilistic modeling and geospatial analysis for nationwide assessment of dietary perchlorate exposure in China: Regional disparities, source contribution, and risk implications.

Environmental pollution (Barking, Essex : 1987)·2026
Same author

A Core Outcome Set for Clinical Trials on Post COVID-19 Condition: "What," "When," and "How" to Measure.

Journal of evidence-based medicine·2025
Same author

Multi-modal medical image synthesis via dual-branch wavelet encoding and deformable feature interaction.

Artificial intelligence in medicine·2025
Same author

Multi-modal semi-supervised medical image segmentation via spatial weight fusion and prototype-based alignment.

Biomedical physics & engineering express·2025
Same author

DBCM-net:dual backbone cascaded multi-convolutional segmentation network for medical image segmentation.

Biomedical physics & engineering express·2025

Related Experiment Video

Updated: Aug 14, 2025

Characterization of Ultra-fine Grained and Nanocrystalline Materials Using Transmission Kikuchi Diffraction
09:13

Characterization of Ultra-fine Grained and Nanocrystalline Materials Using Transmission Kikuchi Diffraction

Published on: April 1, 2017

13.7K

Channel transformer U-Net: an automatic and effective skeleton extraction network for electronic speckle pattern

Biyuan Li, Zhuo Li, Jun Zhang

    Applied Optics
    |January 11, 2023
    PubMed
    Summary

    We developed a deep learning method, Channel Transformer U-Net, to accurately extract skeletons from noisy electronic speckle pattern interferometry (ESPI) fringe patterns. This approach enhances phase extraction accuracy and provides a universal skeleton line marking algorithm.

    More Related Videos

    Electron Channeling Contrast Imaging for Rapid III-V Heteroepitaxial Characterization
    07:50

    Electron Channeling Contrast Imaging for Rapid III-V Heteroepitaxial Characterization

    Published on: July 17, 2015

    11.1K
    Analysis of Tubular Membrane Networks in Cardiac Myocytes from Atria and Ventricles
    10:30

    Analysis of Tubular Membrane Networks in Cardiac Myocytes from Atria and Ventricles

    Published on: October 15, 2014

    20.6K

    Related Experiment Videos

    Last Updated: Aug 14, 2025

    Characterization of Ultra-fine Grained and Nanocrystalline Materials Using Transmission Kikuchi Diffraction
    09:13

    Characterization of Ultra-fine Grained and Nanocrystalline Materials Using Transmission Kikuchi Diffraction

    Published on: April 1, 2017

    13.7K
    Electron Channeling Contrast Imaging for Rapid III-V Heteroepitaxial Characterization
    07:50

    Electron Channeling Contrast Imaging for Rapid III-V Heteroepitaxial Characterization

    Published on: July 17, 2015

    11.1K
    Analysis of Tubular Membrane Networks in Cardiac Myocytes from Atria and Ventricles
    10:30

    Analysis of Tubular Membrane Networks in Cardiac Myocytes from Atria and Ventricles

    Published on: October 15, 2014

    20.6K

    Area of Science:

    • Optical Metrology
    • Image Processing
    • Artificial Intelligence

    Background:

    • Electronic Speckle Pattern Interferometry (ESPI) is crucial for phase extraction.
    • Traditional fringe skeleton extraction in ESPI is challenging due to noise, low contrast, and varied fringe shapes.
    • Accurate skeleton extraction is vital for reliable ESPI phase retrieval.

    Purpose of the Study:

    • To propose a novel deep learning-based method for accurate fringe skeleton extraction from noisy ESPI patterns.
    • To improve the robustness and accuracy of skeleton extraction for enhanced phase retrieval.
    • To develop an automated marking algorithm for skeleton lines in ESPI.

    Main Methods:

    • A deep learning architecture, Channel Transformer U-Net, integrating channel-wise cross fusion transformers.
    • A combined loss function utilizing binary cross entropy and poly focal loss.
    • An automatic skeleton line marking algorithm for phase extraction.

    Main Results:

    • The Channel Transformer U-Net achieved high accuracy (0.9878) and correlation (0.9905) in skeleton extraction.
    • The method successfully extracts accurate, complete, and smooth skeletons from noisy ESPI fringe patterns.
    • The automatic marking algorithm demonstrated strong universality across different ESPI measurements.

    Conclusions:

    • The proposed Channel Transformer U-Net offers a robust solution for accurate ESPI fringe skeleton extraction.
    • The developed method significantly outperforms traditional approaches in handling noisy and low-contrast fringe patterns.
    • The automated marking algorithm enhances the practical applicability of ESPI for various measurement tasks.