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相关概念视频

Sperm Structure and Semen Composition01:22

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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Updated: May 8, 2026

A Seminiferous Tubule Squash Technique for the Cytological Analysis of Spermatogenesis Using the Mouse Model
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深度学习分类方法用于野猪精子形态分析.

Alexandra Keller1,2, McKenna Maus1, Emma Keller1

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

Andrology
|September 17, 2024
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概括
此摘要是机器生成的。

这项研究引入了一种新的人工智能驱动的方法,用于使用基于图像的流细胞计分析野猪精子形态和体健康. 人工智能方法提供客观,快速和准确的精液诊断,改善野猪生育能力评估.

关键词:
一个宏体的宏体.野猪 野猪 野猪 野猪深度学习是一种深度学习.没有标签的 label-free.男人的生育能力 男人的生育能力精子形态和体质 精子形态和体质

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科学领域:

  • 动物科学动物科学
  • 生殖生物学 生殖生物学
  • 生物技术是生物技术.

背景情况:

  • 猪精液质量评估依赖于精子度,运动性和形态学.
  • 目前用于精子形态分析的方法是主观和手动的.
  • 由于人类眼睛的局限性和昂贵的生物标志物分析,缺乏有效检测宏健康状况.

研究的目的:

  • 开发一种新的方法,整合深度学习和基于图像的流细胞计 (IBFC) 来进行客观的精液分析.
  • 为了实现高通量,精子形态的准确评估和无标签的青体健康.
  • 为了克服手工计数和昂贵的生物标记测试在猪精液诊断中的局限性.

主要方法:

  • 使用IBFC捕捉了1万个精子的图像.
  • 手动注释图像用于训练卷积神经网络 (CNN) 关于形态学和体健康.
  • 将训练有素的CNN应用于没有注释的图像,以进行预测和准确性评估.

主要成果:

  • 在形态分类中获得高F1分:96.73% (20x),98.55% (40x) 和99.31% (60x).
  • 在60倍放大时显示出99.8%的F1得分,用于检测微妙的缩体健康变化.
  • 验证了CNN模型在分类精子特征方面的准确性.

结论:

  • 建立了一种集成的AI方法,用于快速分类精子形态和体健康.
  • 展示了人工智能的潜力,以减少技术人员的变化和简化精液测试.
  • 解决了生物标志物采用的障碍,提供了对猪的增强生殖健康评估.