Jove
Visualize
Contact Us

Related Concept Videos

Phase Contrast and Differential Interference Contrast Microscopy01:26

Phase Contrast and Differential Interference Contrast Microscopy

14.7K
Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...
14.7K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.6K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
8.6K

You might also read

Related Articles

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

Sort by
Same author

Impact of an Ophthalmic Cooling Device on Corneal Immune and Sensory Nerve Features in Chronic Ocular Surface Pain: A Prospective, Feasibility Study.

Ophthalmology and therapy·2026
Same author

LIG1 Loss in TP53-mutant Triple Negative Breast Cancer Rewires DNA Repair and Confers Sensitivity to PARP-ATR Inhibitor Combinations.

Molecular cancer therapeutics·2026
Same author

Anterior cerebral artery variants and their influence on endovascular outcomes: a propensity score matched analysis from the CRETA registry.

Neuroradiology·2026
Same author

Editorial: Bioengineering and biotechnology approaches in cardiovascular sciences, volume III.

Frontiers in bioengineering and biotechnology·2026
Same author

Magnetic Resonance Imaging/Diffusion-Weighted Imaging-Guided Versus Perfusion-Guided Intravenous Thrombolysis with Alteplase Beyond 4.5-Hour Window: a Network Meta-Analysis of Randomized Controlled Trials.

Clinical neuroradiology·2026
Same author

Out-of-pocket costs for people with tuberculosis disease in Toronto, Canada: A web-based survey at two tuberculosis treatment centres, April 2023-April 2025.

Canada communicable disease report = Releve des maladies transmissibles au Canada·2026
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 Experiment Video

Updated: Feb 25, 2026

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

12.7K

Automatic phase aberration compensation for digital holographic microscopy based on deep learning background

Thanh Nguyen, Vy Bui, Van Lam

    Optics Express
    |August 10, 2017
    PubMed
    Summary

    We developed an automatic deep learning method for aberration-free quantitative phase imaging in digital holographic microscopy (DHM). This technique enhances real-time measurements of dynamic biological processes like cell migration.

    More Related Videos

    Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization
    10:28

    Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization

    Published on: July 5, 2016

    10.8K
    High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning
    09:31

    High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

    Published on: April 28, 2022

    3.5K

    Related Experiment Videos

    Last Updated: Feb 25, 2026

    Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
    10:16

    Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

    Published on: February 8, 2014

    12.7K
    Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization
    10:28

    Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization

    Published on: July 5, 2016

    10.8K
    High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning
    09:31

    High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

    Published on: April 28, 2022

    3.5K

    Area of Science:

    • Optics and Photonics
    • Biomedical Imaging
    • Computational Biology

    Background:

    • Digital holographic microscopy (DHM) traditionally requires manual aberration compensation, hindering real-time analysis.
    • Existing automatic methods like PCA correct only limited aberrations, failing with complex DHM phase images due to noise and aberrations.
    • Accurate quantitative phase imaging is crucial for studying dynamic biological processes.

    Purpose of the Study:

    • To introduce a fully automatic, deep learning-based technique for aberration-free quantitative phase imaging in DHM.
    • To overcome limitations of manual and existing automatic aberration correction methods in DHM.
    • To enable real-time, high-fidelity phase measurements of dynamic biological samples.

    Main Methods:

    • A novel approach combining supervised deep learning with Convolutional Neural Networks (CNN) and Zernike polynomial fitting (ZPF).
    • The CNN automatically detects background regions in DHM phase images.
    • ZPF is utilized to compute the self-conjugated phase for aberration compensation.

    Main Results:

    • The proposed method achieves automatic background region detection, crucial for quantitative measurements.
    • It effectively compensates for a wider range of aberrations compared to previous methods.
    • The technique demonstrates potential for real-time aberration correction in DHM.

    Conclusions:

    • The integrated deep learning and ZPF method offers a robust solution for aberration-free quantitative phase imaging in DHM.
    • This advancement facilitates accurate, real-time monitoring of dynamic cellular behaviors.
    • The technique addresses key challenges in DHM, improving its applicability in biological research.