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Sperm Structure and Semen Composition

Sperm Structure and Semen Composition

During ejaculation, males release around 2-5 milliliters of semen, which is a complex mixture of mature sperm and various fluids produced by accessory glands. The mature sperm cells measure approximately 60 micrometers in length and consist of a head, neck, midpiece, and tail. The head is flattened and tapered, measuring about 4 to 5 micrometers in length. It contains a nucleus with condensed chromosomes and an acrosome, a cap-like structure filled with enzymes essential for penetrating the...

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A Seminiferous Tubule Squash Technique for the Cytological Analysis of Spermatogenesis Using the Mouse Model

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Published on: February 6, 2018

Deep learning classification method for boar sperm morphology analysis.

Alexandra Keller^1,2, McKenna Maus¹, Emma Keller¹

¹Department of Animal Science, Iowa State University, Ames, Iowa, USA.

|September 17, 2024

Summary

This summary is machine-generated.

This study introduces a novel AI-powered method for analyzing boar sperm morphology and acrosome health using image-based flow cytometry. The AI approach offers objective, rapid, and accurate semen diagnostics, improving boar fertility assessments.

Keywords:

acrosome boar deep learning label‐free male fertility sperm morphology

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Area of Science:

Animal Science
Reproductive Biology
Biotechnology

Background:

Boar semen quality assessment relies on sperm concentration, motility, and morphology.
Current methods for sperm morphology analysis are subjective and manual.
Efficient detection of acrosome health is lacking due to human eye limitations and costly biomarker analysis.

Purpose of the Study:

To develop a novel approach integrating deep learning and image-based flow cytometry (IBFC) for objective semen analysis.
To enable high-throughput, accurate assessment of sperm morphology and label-free acrosome health.
To overcome limitations of manual counting and costly biomarker assays in boar semen diagnostics.

Main Methods:

Captured images of 10,000 spermatozoa using IBFC.
Manually annotated images for training a convolutional neural network (CNN) on morphology and acrosome health.
Applied the trained CNN to unannotated images for prediction and accuracy assessment.

Main Results:

Achieved high F1 scores for morphological classification: 96.73% (20x), 98.55% (40x), and 99.31% (60x).
Demonstrated a 99.8% F1 score for detecting subtle acrosome health variations at 60x magnification.
Validated the CNN model's accuracy in classifying sperm characteristics.

Conclusions:

Established an integrated AI approach for rapid classification of sperm morphology and acrosome health.
Demonstrated the potential of AI to reduce technician variability and streamline semen assays.
Addressed barriers to biomarker adoption, offering enhanced reproductive health assessments in swine.