Staining Correction in Digital Pathology with Dye Amount Look-Up Table

P. Bautista, Y. Yagi
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Abstract

Automated image analysis of histology images is affected by the staining variations in histology slides. In general, training images are used to optimize the parameters of an image analysis system. Color, being one of the dominant features of stained tissue samples, is being commonly utilized as feature to segment or classify the different stained tissue components. However, the colors impressed on the tissue components vary with the staining condition of the sample. Hence, when the staining conditions of the slides for the training and test images differ, the accuracy of the analysis results would likely degrade. In this work we present a method to correct the staining condition of the histology images by constructing a look-up table (LUT) of the stained pixels' dye amounts. The present method allows the user to not only correct the staining condition of a given histology image with respect to the staining condition of the reference slide, but to also recreate his/her preferred staining condition for the given image. The results of our experiments with hematoxylin and eosin (H&E) stained tissue images showed the effectiveness of the present method.
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染色量查表在数字病理染色校正中的应用
组织学图像的自动图像分析受组织学切片染色变化的影响。一般来说,训练图像是用来优化图像分析系统的参数。颜色作为染色组织样本的主要特征之一,被广泛用作对不同染色组织成分进行分割或分类的特征。然而,印在组织成分上的颜色随样品的染色条件而变化。因此,当训练图像和测试图像的载玻片染色条件不同时,分析结果的准确性可能会降低。在这项工作中,我们提出了一种方法,通过构建一个查找表(LUT)的染色像素的染料量来纠正组织学图像的染色条件。本方法允许用户不仅校正相对于参考载玻片的染色条件的给定组织学图像的染色条件,而且还重建他/她对给定图像的首选染色条件。苏木精和伊红(H&E)染色组织图像的实验结果表明了该方法的有效性。
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