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Related Concept Videos

Flow Cytometry01:23

Flow Cytometry

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The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Related Experiment Video

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High-Dimensionality Flow Cytometry for Immune Function Analysis of Dissected Implant Tissues
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Toward lower contrast computer vision in vivo flow cytometry.

Stacey Markovic, Siyuan Li, Tianxue Zhang

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 9, 2015
    PubMed
    Summary

    We developed a novel optical flow phantom to improve in vivo flow cytometry (IVFC) for detecting circulating cells (CCs). This phantom helps optimize imaging systems for low-contrast conditions, enhancing cell detection sensitivity.

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    Area of Science:

    • Biomedical Engineering
    • Optical Imaging
    • Cellular Biology

    Background:

    • Circulating cell detection is crucial in biomedical research.
    • Existing methods require ex vivo analysis of blood samples.
    • In vivo flow cytometry (IVFC) offers real-time detection in small animals.

    Purpose of the Study:

    • To optimize Computer Vision-IVFC (CV-IVFC) for low-contrast imaging conditions.
    • To develop a reliable phantom model for simulating in vivo conditions.
    • To enhance the sensitivity and accuracy of circulating cell enumeration.

    Main Methods:

    • Developed a novel optical flow phantom with controllable autofluorescence and physical structure.
    • Acquired image sequences from phantoms mimicking in vivo mouse conditions.
    • Analyzed image data to characterize CV-IVFC algorithm performance (sensitivity, false-alarm rates).

    Main Results:

    • The phantom successfully replicated low-contrast imaging scenarios.
    • CV-IVFC algorithm performance was characterized under simulated conditions.
    • The phantom model provides a controlled environment for system optimization.

    Conclusions:

    • The developed optical flow phantom is effective for optimizing IVFC systems.
    • This model aids in improving circulating cell detection under challenging imaging conditions.
    • Further research will focus on refining the instrument and algorithm using this phantom.