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基于深度学习的图像细胞测量使用比特模式内核过算法,以避免多次计数的细胞确定.

Tomoki Abe1, Kimihiro Yamashita2, Toru Nagasaka3,4

  • 1Division of Gastrointestinal Surgery, Department of Surgery, Graduate School of Medicine, Kobe University, Kobe, Japan.

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概括

基于深度学习的图像细胞计 (DL-IC) 准确地识别数字化病理幻灯片中的细胞. 免疫组织化学染色增强了早期学习,提高了精确瘤学的诊断潜力.

关键词:
深度学习是一种深度学习.图像细胞计量图像细胞计量免疫组织化学染色

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科学领域:

  • 病理学 病理学 病理学
  • 计算生物学 计算生物学
  • 在瘤学瘤学.

背景情况:

  • 组织幻灯片的数字化和深度学习图像分析增强了病理诊断和患者管理.
  • 基于深度学习的图像细胞计量 (DL-IC) 对于精确的细胞识别和数字病理学的计数至关重要.
  • 精确的细胞测定对于DL-IC技术的有效应用至关重要.

研究的目的:

  • 评估一个新的DL-IC系统,Cu-Cyto在细胞识别中的性能.
  • 评估免疫组织化学染色对DL-IC性能的影响.
  • 确定DL-IC在推进精密瘤学的潜力.

主要方法:

  • 开发Cu-Cyto,一个DL-IC,使用比特模式内核过算法来防止细胞计数错误.
  • 评估Cu-Cyto在瘤组织幻灯片图像上的性能,使用免疫组织化学染色 (IHC).
  • 对不同学习阶段的3个Cu-Cyto版本的评估.

主要成果:

  • 在训练初期,Cu-Cyto对免疫染的CD8+T细胞 (0.343) 的F1得分较高,与非免疫染细胞 (腺癌:0.040,淋巴细胞:0.002) 相比.
  • 随着培训和验证的进展,所有细胞类型的性能都有所改善.
  • 在最后的学习阶段,F1得分达到0.589的腺癌细胞,0.889的淋巴细胞和0.911的CD8+T细胞.

结论:

  • Cu-Cyto 证明了有效的细胞鉴定能力.
  • 免疫组织化学染色显著提高DL-IC学习效率,特别是在早期阶段.
  • 预计持续的学习将进一步提高Cu-Cyto的性能,支持精确瘤学的实施.