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A Stained-Free Sperm Morphology Measurement Method Based on Multi-Target Instance Parsing and Measurement Accuracy

Miao Hao1, Rongan Zhai2, Yong Wang3

  • 1Research Center of Robotics and Micro Systems, School of Mechanical and Electrical Engineering, Soochow University, Suzhou 215021, China.

Sensors (Basel, Switzerland)
|February 13, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an automated, non-invasive method for sperm morphology analysis using a novel network. The approach enhances accuracy in measuring sperm features, improving male infertility diagnosis and assisted reproductive technologies (ARTs).

Keywords:
morphological analysisnon-invasivestained-free sperm

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Area of Science:

  • Reproductive Biology
  • Medical Imaging
  • Computational Biology

Background:

  • Sperm morphology assessment is crucial for male infertility diagnosis and assisted reproductive technologies (ARTs).
  • Traditional manual methods are subjective, inconsistent, and involve cell-damaging staining.
  • There is a need for automated, accurate, non-invasive sperm morphology analysis.

Purpose of the Study:

  • To develop and validate a novel automated method for non-stained sperm morphology analysis.
  • To improve the accuracy and consistency of sperm morphological parameter measurements.
  • To overcome the limitations of traditional manual semen analysis.

Main Methods:

  • A multi-scale part parsing network integrating semantic and instance segmentation for instance-level sperm parsing.
  • A measurement accuracy enhancement strategy using statistical analysis and signal processing (IQR, Gaussian filtering).
  • Quantitative analysis of sperm head, midpiece, and tail morphological parameters.

Main Results:

  • The proposed multi-scale part parsing network achieved 59.3% APvolp, outperforming state-of-the-art by 9.20%.
  • The measurement accuracy enhancement strategy significantly reduced errors in head, midpiece, and tail measurements by up to 35.0%.
  • The method enables precise, non-invasive morphological parameter measurement for individual sperm.

Conclusions:

  • The novel automated method offers a significant advancement over traditional manual sperm morphology assessment.
  • This technique provides accurate, non-invasive, and consistent analysis, benefiting male infertility diagnosis and ART.
  • The combined network and enhancement strategy effectively addresses limitations in current semen analysis.