Grading nuclear pleomorphism on histological micrographs
Grading nuclear pleomorphism on histological micrographs
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DOI：
10.1109/icpr.2008.4761112
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发表时间：
2008-12
期刊：
影响因子：
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通讯作者：
E. Cosatto;Matthew L. Miller;H. Graf;J. Meyer
中科院分区：
文献类型：
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作者：
E. Cosatto;Matthew L. Miller;H. Graf;J. Meyer
癌症诊断中的支柱是基于其外观对细胞核进行分类或分级。虽然细胞学样本的分析已经成功地自动化了很长一段时间，但组织学组织样本的复杂性阻碍了机器视觉技术的可靠分类。我们在多个阶段处理这个复杂的问题，首先分析图像质量，染色质量和组织外观，然后分割细胞核，最后对组织区域进行分类或分级。其中关键的一步是训练一个分类器来判断细胞核的分割质量。使用主动学习技术，我们训练这个分类器来识别图像中的问题以及图像分析工具的弱点。通过这种方式，我们获得了强大的核分割，可以精确测量可以安全用于分类的特征。我们在数百例乳腺癌病例中验证了我们的研究结果，表明自动多形性分级具有很高的准确性。这种技术可以为再现性低的主观过程提供稳定和客观的基础。
A mainstay in cancer diagnostics is the classification or grading of cell nuclei based on their appearance. While the analysis of cytological samples has been automated successfully for a long time, the complexity of histological tissue samples has prevented a reliable classification with machine vision techniques. We approach this complex problem in multiple stages, analyzing first image quality, staining quality, and tissue appearance, before segmenting nuclei and finally classifying or grading areas of tissue. The key step is the training of a classifier to judge the nuclei segmentation quality. Using active learning techniques, we train this classifier to identify problems in the image as well as weaknesses of the image analysis tools. This way we obtain robust nuclear segmentation allowing precise measurements of features that can be used safely for classification. We validate our findings on several hundred cases of breast cancer, demonstrating that automatic pleomorphism grading is possible with high accuracy. This technique can provide a stable and objective basis for what has been a subjective process that suffers from low reproducibility.