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Related Concept Videos

Meiosis II01:57

Meiosis II

Meiosis II is the second and final stage of meiosis. It relies on the haploid cells produced during meiosis I, each of which contain only 23 chromosomes—one from each homologous initial pair. Importantly, each chromosome in these cells is composed of two joined copies, and when these cells enter meiosis II, the goal is to separate such sister chromatids using the same microtubule-based network employed in other division processes. The result of meiosis II is two haploid cells, each containing...
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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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Related Experiment Video

Updated: May 31, 2026

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes
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A two-stage sperm holomorphological analysis method based on multi-output network construction.

Wentan Jiao1, Mengqing Hu1, Bo Wang2

  • 1School of Electronic Information, Luoyang Institute of Science and Technology, Luoyang, China.

BMC Bioinformatics
|May 29, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an automated sperm morphology analysis using deep learning, improving detection accuracy for male infertility diagnosis. The novel two-stage method enhances efficiency and objectivity in sperm analysis.

Keywords:
Bilinear poolingDeep learningMulti-output classificationSperm morphology analysis

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Area of Science:

  • Reproductive Medicine
  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare

Background:

  • Sperm morphology analysis is crucial for diagnosing male infertility.
  • Traditional manual methods are subjective and inefficient.
  • Deep learning offers objective and efficient sperm detection.

Purpose of the Study:

  • To develop an automated, objective, and efficient sperm morphology analysis method.
  • To improve the accuracy of sperm detection for male infertility diagnosis.
  • To leverage deep learning for end-to-end sperm analysis.

Main Methods:

  • A two-stage approach using UNet++ for sperm segmentation.
  • Multi-Head Mobilevit Net with multi-output classification for detecting sperm head, midpiece, and principal piece.
  • Integration of the Mixing Loss function into the classification model.

Main Results:

  • Achieved high accuracy rates: 83.47% for sperm head, 96.19% for midpiece, and 94.99% for principal piece.
  • Utilized an expanded dataset of 10,802 sperm images with data enhancement.
  • Demonstrated the effectiveness of the two-stage deep learning model.

Conclusions:

  • The proposed method enables rapid automated detection of sperm morphology.
  • Provides robust technical support for advancements in reproductive medicine.
  • Highlights the potential of deep learning in clinical diagnostics.