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Related Concept Videos

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

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Related Experiment Video

Updated: May 7, 2026

Using Computer Vision Libraries to Streamline Nuclei Quantification
06:25

Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

Automated seeding-based nuclei segmentation in nonlinear optical microscopy.

Anna Medyukhina, Tobias Meyer, Sandro Heuke

    Applied Optics
    |October 3, 2013
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an improved nuclei segmentation method for nonlinear optical (NLO) microscopy, enhancing diagnostic accuracy in biomedical imaging. The optimized approach ensures reliable cell nucleus identification in diverse tissue types.

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    Area of Science:

    • Biomedical imaging
    • Microscopy
    • Computational pathology

    Background:

    • Nonlinear optical (NLO) microscopy, including coherent anti-Stokes Raman scattering (CARS) and two-photon-excited fluorescence (TPEF), offers rapid, label-free imaging with significant biomedical potential.
    • Current diagnostic applications of NLO microscopy are limited by the absence of robust nuclei segmentation algorithms adaptable to varying image contrast, nuclear density, and tissue types.

    Purpose of the Study:

    • To develop and validate an optimal seeding-based nuclei segmentation approach for NLO microscopy images.
    • To compare the efficacy of different seeding and boundary detection techniques for accurate segmentation of both TPEF and CARS NLO microscopy data.

    Main Methods:

    • Comparative analysis of various seeding and boundary detection algorithms applied to NLO microscopy images.
    • Implementation of a Laplacian of Gaussian filter for optimal image seeding.
    • Application of a modified seeded watershed segmentation for accurate boundary detection.
    • Post-segmentation verification of detected nuclei.

    Main Results:

    • The Laplacian of Gaussian filter demonstrated superior accuracy for seeding NLO microscopy images.
    • A modified seeded watershed segmentation proved most effective for boundary detection.
    • The combined approach achieved high average sensitivity and specificity across different NLO microscopy image types.

    Conclusions:

    • The developed seeding-based segmentation method, utilizing Laplacian of Gaussian filtering and modified seeded watershed segmentation, provides accurate and robust nuclei identification in NLO microscopy.
    • This optimized algorithm is a crucial step towards the routine clinical use of NLO microscopy in pathology and surgical guidance.