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基于深度神经网络的人类卵细胞图像分类方法.

Anna Targosz1,2, Dariusz Myszor3, Grzegorz Mrugacz4

  • 1Department of Histology and Embryology, Faculty of Medical Sciences, Medical University of Silesia, 18 Medyków St, 40-752, Katowice, Poland. atargosz@klinikabocian.pl.

Biomedical engineering online
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概括

这项研究引入了一种使用深度神经网络 (DNN) 来分类人类卵细胞的自动化方法,提高了体外受精成功率. DNN模型在通过显微镜图像识别卵细胞成熟阶段方面取得了很高的准确性.

关键词:
人工智能的人工智能是人工智能.分类 分类 分类 分类.深度神经网络是一个神经网络.人类卵子细胞在这个过程中,IVF IVF.机器学习是机器学习.

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科学领域:

  • 生殖生物学 生殖生物学
  • 生物医学工程 生物医学工程
  • 医学中的人工智能.

背景情况:

  • 在体外受精 (IVF) 的成功取决于选择具有高发育潜力的卵细胞和胚胎.
  • 精确分类卵细胞中介成熟度对于细胞内精子注射 (ICSI) 至关重要.
  • 在显微镜下传统的手动卵细胞分类是主观的,耗时的.

研究的目的:

  • 开发一种基于显微镜图像对人类卵细胞进行分类的自动化系统.
  • 提高辅助生殖技术的卵细胞选择的效率和准确性.
  • 为了利用深度神经网络 (DNN) 算法进行卵细胞成熟度评估.

主要方法:

  • 采用两阶段深度神经网络方法进行自动化卵细胞分类.
  • 使用DeepLabV3Plus从卵细胞图像中提取特征.
  • 应用了一个以SqueezeNet为灵感的网络,通过遗传算法进行优化,用于卵细胞类型分类 (MI,MII,PI).

主要成果:

  • 在验证组中达到0.964的分类准确度,在测试组中达到0.957的分类准确度.
  • 遗传算法改进了网络,以提高概括性和降低计算成本 (FLOP).
  • 开发的管道使人类卵细胞能够自动分类为MI,MII和PI阶段.

结论:

  • 从显微镜图像中成功开发了一种自动化人类卵细胞分类的完整管道.
  • 该自动化系统为提高IVF和ICSI程序中的卵细胞选择提供了一个有前途的工具.
  • 公开发布的代码和训练有素的神经网络有助于进一步的研究和临床应用.