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

2.8K
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...
2.8K

You might also read

Related Articles

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

Sort by
Same author

The role of the Wnt/BDNF pathway in maternal SCH-induced autism-like phenotypes in offspring rats: behavioral and molecular mechanisms.

Translational psychiatry·2025
Same author

Analysis of three-dimensional pinhole diffraction of deep ultraviolet converging light with a large numerical aperture.

Applied optics·2025
Same author

Self-focusing high-frequency ultrasonic transducers for non-destructive testing applications.

Scientific reports·2025
Same author

PCLLA-nanoHA Bone Substitute Promotes M2 Macrophage Polarization and Improves Alveolar Bone Repair in Diabetic Environments.

Journal of functional biomaterials·2023
Same author

Gas-Liquid Mass Transfer Behavior of Upstream Pumping Mechanical Face Seals.

Materials (Basel, Switzerland)·2022
Same author

The Role of Autophagy in Lamellar Body Formation and Surfactant Production in Type 2 Alveolar Epithelial Cells.

International journal of biological sciences·2022

Related Experiment Video

Updated: Jan 1, 2026

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.6K

Machine-learning models for analyzing TSOM images of nanostructures.

Yufu Qu, Jialin Hao, Renju Peng

    Optics Express
    |December 28, 2019
    PubMed
    Summary

    This study introduces a machine learning approach to enhance dimensional measurement accuracy in through-focus scanning optical microscopy (TSOM) for 3D nanostructures. The new method significantly outperforms traditional library-matching techniques.

    More Related Videos

    Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images
    12:45

    Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images

    Published on: August 31, 2022

    3.5K
    Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper
    07:38

    Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

    Published on: April 9, 2017

    10.4K

    Related Experiment Videos

    Last Updated: Jan 1, 2026

    Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
    12:06

    Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

    Published on: March 3, 2023

    4.6K
    Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images
    12:45

    Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images

    Published on: August 31, 2022

    3.5K
    Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper
    07:38

    Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

    Published on: April 9, 2017

    10.4K

    Area of Science:

    • Nanotechnology
    • Optical Microscopy
    • Machine Learning

    Background:

    • Through-focus scanning optical microscopy (TSOM) is a cost-effective, non-destructive technique for 3D nanostructure measurement.
    • Current TSOM analysis relies on library-matching, which can limit dimensional measurement accuracy.

    Purpose of the Study:

    • To develop and evaluate a machine learning (ML) method for improving dimensional measurement accuracy in TSOM.
    • To extract texture information from TSOM images using feature vectors for enhanced analysis.

    Main Methods:

    • Feature extraction using Gray-level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Histogram of Oriented Gradient (HOG).
    • Training and testing of three ML regression models: Random Forest, Gradient Boosting Decision Tree (GBDT), and AdaBoost.
    • Evaluation of feature vectors used in isolation, in pairs, and in combination.

    Main Results:

    • The proposed ML method demonstrates considerably higher measurement accuracy compared to the library-matching method.
    • The AdaBoost model with combined LBP and HOG features excels in measuring features across a wide size range.
    • For narrower size ranges, the AdaBoost model with HOG features shows superior performance.

    Conclusions:

    • Machine learning, particularly using texture features like LBP and HOG, significantly enhances dimensional measurement accuracy in TSOM.
    • The choice of feature extraction method and ML model can be optimized based on the size range of the nanostructure features being measured.