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Automatic Tracking and Motility Analysis of Human Sperm in Time-Lapse Images.

Leonardo F Urbano, Puneet Masson, Matthew VerMilyea

    IEEE Transactions on Medical Imaging
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    PubMed
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    We developed an automated algorithm to track hundreds of sperm cells simultaneously, measuring their motility. This technology aids in correlating sperm swimming dynamics with human fertility rates.

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    Area of Science:

    • Biomedical Engineering
    • Reproductive Biology
    • Computer Vision

    Background:

    • Semen analysis is crucial for assessing male fertility.
    • Current computer-assisted semen analysis (CASA) systems have limitations in tracking multiple sperm cells accurately, especially in close proximity or during collisions.
    • Manual motility measurements are time-consuming and operator-dependent.

    Purpose of the Study:

    • To develop and validate a fully automated multi-sperm tracking algorithm for enhanced semen analysis.
    • To accurately measure human sperm motility parameters over time with minimal operator intervention.
    • To explore the association between dynamic sperm swimming parameters and fertility/fertilization rates.

    Main Methods:

    • An image processing method based on radar tracking algorithms was adapted.
    • The joint probabilistic data association filter (JPDAF) was utilized for simultaneous tracking of hundreds of sperm cells.
    • Timelapse microscopy image sequences were analyzed to detect and track sperm swimming paths.
    • Motility parameters were continuously collected without sample dilution.

    Main Results:

    • The algorithm successfully detected and tracked hundreds of sperm cells simultaneously in video recordings.
    • Accurate measurement of dynamic sperm motility parameters over time was achieved.
    • The system demonstrated capability in tracking sperm in close proximity and during apparent collisions, overcoming limitations of existing CASA systems.
    • Continuous data collection without sample dilution was enabled.

    Conclusions:

    • The automated multi-sperm tracking algorithm offers a significant advancement in semen analysis.
    • This technology has the potential to improve the accuracy and efficiency of fertility assessments.
    • It provides researchers and clinicians with more comprehensive data for understanding sperm function and its relation to fertility outcomes.