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Measuring Sperm Guidance and Motility within the Caenorhabditis elegans Hermaphrodite Reproductive Tract
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基于多重目标追踪的活精子的多维形态分析.

Hao Yang1, Mengmeng Ma1, Xiangfeng Chen2,3,4

  • 1Department of Assisted Reproduction, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, China.

Computational and structural biotechnology journal
|January 13, 2025
PubMed
概括

这项研究引入了用于自动化精子分析的深度学习框架,提高了评估辅助生殖的精子运动性和形态学的准确性和效率.

关键词:
深度学习是一种深度学习.活的精子是活的精子.形态学分析 形态学分析移动性 移动性 移动性多种精子跟踪多种精子跟踪没有染色,不染色.

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科学领域:

  • 生物医学工程 生物医学工程
  • 人工智能在医学中的应用
  • 生殖生物学 生殖生物学

背景情况:

  • 手动的精液评估是主观的和低效的.
  • 目前的方法阻碍了辅助生殖技术 (ART) 的进步.
  • 需要客观的,自动化的精子分析.

研究的目的:

  • 开发一种深度学习框架,用于对运动中的活精子进行非侵入性,多维分析.
  • 为了增强目前的临床精子形态测试.
  • 为促进ART的发展做出贡献.

主要方法:

  • 改进了FairMOT跟踪算法,增强了精子头部运动分析的成本功能.
  • 使用BlendMask用于单个精子细分和SegNet用于形态成分分离.
  • 在来自多家三级医院的1272个样本上验证了该系统.

主要成果:

  • 获得了90.82%的形态准确度,经验丰富的医生证实了这一点.
  • 在自动化系统结果和手动显微镜之间证明了很高的一致性.
  • 成功实现了精子进展性运动和形态学的同时自动检测.

结论:

  • 深度学习框架为手动精液评估提供了一个客观而有效的替代方案.
  • 这项技术可以显著改善精子选择,用于诸如cytoplascitc精子注射 (ICSI) 等程序.
  • 该系统推进了自动化精子分析,以改善ART结果.