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A framework for evaluating predicted sperm trajectories in crowded microscopy videos.

David Hart1, Kylie Cashwell2, Anita Bhandari1

  • 1Department of Computer Science, East Carolina University, Greenville, North Carolina, United States of America.

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Accurate sperm tracking is crucial for analyzing motility patterns. This study introduces a new framework to assess sperm tracking quality, improving predictive models for sperm fertilizing competence.

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Area of Science:

  • Reproductive Biology
  • Biomedical Engineering
  • Computational Biology

Background:

  • Semi-automated sperm motility analysis relies on accurate sperm tracking in microscopy videos.
  • Current methods often require sample dilution and short observation times, limiting analysis of long-term motility patterns.
  • Accurate tracking over physiologically relevant timescales is needed to improve predictive models for sperm fertilizing competence.

Purpose of the Study:

  • To develop a framework for assessing sperm trajectory tracking quality independent of standard motility measures.
  • To adapt and modify cell tracking metrics for the specific challenges of sperm video-microscopy.
  • To provide a labeled dataset for future research in sperm tracking.

Main Methods:

  • Adapted cell tracking metrics from adherent somatic cell tracking.
  • Modified metrics for sperm video-microscopy challenges like high cell density and crossing trajectories.
  • Developed a framework to evaluate tracking quality using these metrics.
  • Created a dataset of 340 labeled sperm trajectories.

Main Results:

  • The proposed framework accurately assesses sperm trajectory tracking quality.
  • Modifications to tracking metrics improved performance.
  • Configuration variations led to up to a 30% improvement in tracking analysis metrics.
  • A new dataset for sperm tracking research was provided.

Conclusions:

  • The developed framework offers a robust method for evaluating sperm tracking quality.
  • This approach can enhance the accuracy and predictive value of computer-assisted semen analysis.
  • The findings facilitate more reliable long-term motility analysis for assessing sperm fertilizing potential.