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通过升级低质量的注释来降低细胞分割的手动注释成本.

Serban Vădineanu1, Daniël M Pelt1, Oleh Dzyubachyk2

  • 1Leiden Institute of Advanced Computer Science, Leiden University, 2311 EZ Leiden, The Netherlands.

Journal of imaging
|July 26, 2024
PubMed
概括

本研究介绍了一种方法,使用深度学习来改进低质量的细胞图像注释,显著降低注释成本并提高细分网络性能. 该方法将标签升级为遗漏,包含或偏见错误,实现与基本真相高度相似.

关键词:
增加了注释的增强功能.有注释错误的注释错误细胞细分 细胞细分 细胞细分深度学习是一种深度学习.

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科学领域:

  • 计算生物学 计算生物学
  • 医学成像分析 医学成像分析
  • 人工智能在医学中的应用

背景情况:

  • 针对细胞细分的深度学习需要广泛的,高质量的注释数据集,这些数据集的生产成本高,耗时长.
  • 高昂的注释成本可能是开发准确的细胞细分模型的重要障碍.

研究的目的:

  • 开发一种深度学习方法来升级低质量的细胞图像注释,从而减少注释时间和成本.
  • 评估拟议方法在纠正各种类型的注释错误 (遗漏,包含,偏差) 中的有效性.
  • 为了证明升级注释在训练改进的细胞细分网络中的实用性.

主要方法:

  • 在一个小而高质量的数据集上训练了一个卷积神经网络,以学习如何升级低质量的注释.
  • 该方法在模拟遗漏,包含和偏差错误的注释上进行了测试.
  • 使用Dice相似系数与基准真相注释来量化性能.

主要成果:

  • 提出的方法成功地升级了具有高错误级别的注释,实现了高达0.9.9的Dice相似度得分.
  • 训练细胞细分网络的组合,以及注释和升级的数据导致更好的性能,而不是只使用注释的数据集.
  • 一个用例证明了从仅在10个注释样本上训练的网络中预测的质量成功提高.

结论:

  • 深度学习可以有效地升级低质量的细胞图像注释,显著降低注释成本.
  • 通过升级注释来扩大小,高质量的数据集,可以提高细胞细分模型的性能.
  • 这种方法提供了一种实际的解决方案,用于在资源有限的环境中增强细胞图像分析.