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Related Concept Videos

Spermatogenesis01:41

Spermatogenesis

Spermatogenesis is the process by which haploid sperm cells are produced in the male testes. It starts with stem cells located close to the outer rim of seminiferous tubules. These spermatogonial stem cells divide asymmetrically to give rise to additional stem cells (meaning that these structures “self-renew”), as well as sperm progenitors, called spermatocytes. Importantly, this method of asymmetric mitotic division maintains a population of spermatogonial stem cells in the male reproductive...

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AndroGen: Open-source synthetic data generation for automated sperm analysis.

Daniel Hernández-Ferrándiz1, Juan J Pantrigo1, Soto Montalvo1

  • 1Universidad Rey Juan Carlos, Móstoles, Spain.

Computer Methods and Programs in Biomedicine
|October 31, 2025
PubMed
Summary

Researchers can now generate customized synthetic sperm images for machine learning using AndroGen, an open-source tool. This overcomes limitations of real data acquisition, enabling diverse and privacy-compliant datasets for automated sperm analysis.

Keywords:
Medical imagingOpen source softwareSperm analysisSynthetic data

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Area of Science:

  • Reproductive biology
  • Computer vision
  • Machine learning

Background:

  • Automated sperm analysis relies on machine learning models trained on large, diverse datasets.
  • Acquiring real microscopic samples is costly, time-consuming, and raises privacy concerns.
  • Existing methods often require extensive real data or complex generative models.

Purpose of the Study:

  • Introduce AndroGen, an open-source software tool for generating customized synthetic male reproductive cell images.
  • Provide researchers with a practical solution for creating labeled sperm datasets.
  • Address the limitations of real data acquisition in machine learning for sperm analysis.

Main Methods:

  • AndroGen features a user-friendly graphical interface with predefined configurations.
  • Offers dialogue controls for users to customize dataset parameters without extensive real images or generative models.
  • The system architecture is detailed for transparency and reproducibility.

Main Results:

  • AndroGen's performance was validated using quantitative (Fréchet Inception Distance, Kernel Inception Distance) and qualitative metrics.
  • Case studies demonstrated the tool's ability to generate realistic synthetic image datasets.
  • Generated images showed high similarity to real microscopic samples.

Conclusions:

  • AndroGen provides a fast and interactive method for creating realistic, labeled sperm datasets.
  • The tool allows customization of cell morphology and movement for tailored dataset generation.
  • Facilitates the development and improvement of automated sperm analysis systems.