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基于增强的深度学习用于识别循环瘤细胞.

Martina Russo1, Giulia Bertolini2, Vera Cappelletti2

  • 1Institute for High Performance Computing and Networking-National Research Council of Italy (ICAR-CNR), Naples, Italy.

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概括

深度学习使用明亮场图像准确识别血液中的循环瘤细胞 (CTC),改善液体活检诊断. 这种方法通过克服传统光技术的局限性,提高了癌症患者的管理.

关键词:
增强 增强是一种增强.癌症 癌症 癌症 癌症循环中的瘤细胞.德帕拉雷 (DEParray) 是一个发达国家.深度学习是一种深度学习.发生转移的转移.

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科学领域:

  • 生物医学工程 生物医学工程
  • 计算生物学 计算生物学
  • 在瘤学瘤学.

背景情况:

  • 循环瘤细胞 (CTC) 是非侵入性癌症管理的重要液体活检生物标志物.
  • 鉴定CTC是具有挑战性的,因为其数量较少,异质,以及基于光的方法的局限性.
  • 手动分析单细胞图像是耗时且可变的.

研究的目的:

  • 开发深度学习 (DL) 管道,以使用明亮场图像将CTC与白细胞区分开来.
  • 为了提高诊断准确度,并优化临床工作流程用于CTC分析.
  • 通过使用明亮场成像来克服光方法的局限性.

主要方法:

  • 使用Parsortix®和DEPArrayTM技术进行公正的CTC隔离和单细胞可视化.
  • 开发了一个基于ResNet的卷积神经网络 (CNN) 用于图像分类.
  • 在培训期间应用数据增强和集成光 (DAPI) 通道图像,以提高概括性.

主要成果:

  • 在区分CTC与白细胞方面,DL模型获得了0.798的F1得分.
  • 该模型在测试期间仅使用明亮场图像成功识别了CTC.
  • 证明了DL在没有光标记的情况下改进CTC分析的潜力.

结论:

  • 深度学习提供了一个强大的工具,用于在液体活检中准确识别CTC.
  • 光场单细胞分析与DL相结合,可以克服CTC检测当前的局限性.
  • 这种方法有可能显著推进癌症患者的管理和诊断.