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在免疫组织化学图像中使用机器学习进行自动细胞分类和量化

Pikting Cheung1, Wei Zhang2, Muhammad Shehzad Khan1,3

  • 1Department of Physics, City University of Hong Kong, Hong Kong, SAR, China.

Journal of histotechnology
|July 1, 2025
PubMed

Insights

一种创新的数学方法精确地量化了免疫组织化学 (IHC) 图像中的淋巴瘤细胞. 这种自动化方法提高了淋巴瘤分类的诊断准确性,减少了人为错误.

科学领域:

  • 在瘤学瘤学.
  • 计算病理学计算病理学
  • 医学成像分析 医学成像分析

背景情况:

  • 淋巴瘤的发病率正在增加,需要准确的分类方法.
  • 免疫组织化学 (IHC) 对于淋巴瘤的分类至关重要.
  • 在IHC图像中手动计数细胞是耗时且容易出现错误的.

研究的目的:

  • 开发一种自动化的数学方法来精确量化和空间分析CD3染色淋巴瘤IHC图像中的免疫阳性和免疫阴性细胞.
  • 减少人类干预,提高淋巴瘤诊断中细胞计数的准确性.

主要方法:

  • 开发了一种使用数学色彩模型进行细胞分化的算法.
  • 采用了形态侵蚀,算法转换和定制的直方形平衡来增强功能.
  • 使用精细的局部值来提高分类精度.
  • 应用了一个定制的圆形Hough变换用于细胞计数和空间数据评估.

主要成果:

  • 在IHC图像样本中实现了93.98%的自动细胞计数的整体准确性.
  • 自动计数和位置信息由三个病理学专家进行了交叉验证.
  • 证明了自动化方法的有效和可靠性能.

结论:

  • 这种创新框架提高了IHC图像中淋巴瘤细胞计数的准确性.
  • 将基于物理的颜色理解与机器学习相结合,以改善诊断.
  • 降低了淋巴瘤分类中人为错误的风险.
抽象的

No abstract available in PubMed .

关键词:
细胞量化的量化.计算病理学计算病理学免疫组织化学 免疫组织化学淋巴瘤淋巴瘤是什么机器学习是机器学习.

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