Staining Correction in Digital Pathology by Utilizing a Dye Amount Table
Staining Correction in Digital Pathology by Utilizing a Dye Amount Table
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DOI：
10.1007/s10278-014-9766-0
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发表时间：
2015-06-01
影响因子：
4.4
通讯作者：
Yagi, Yukako
中科院分区：
文献类型：
--
作者：
Bautista, Pinky A.;Yagi, Yukako
组织成分的染色颜色通常用作图像分析的特征。但是，组织学的染色条件的变化促使染色组织样品的颜色分布促进了变化，这可能会影响分析的准确性。在本文中，我们提出了一种纠正组织学图像染色状况的方法。在该方法中，建立了基于样品吸收的染料量的查找表（LUT）。 （i）指定源和参考染色条件，或者（ii）用户只想重新创建其首选的染色条件而不指定任何参考幻灯片时，就可以构建LUT。在两个方面评估了本方法的有效性：（i）核组织的十个不同载玻片之间的核，细胞质和红细胞的Cielab色素差异，以及（ii）不同组织成分的分类。当前染色校正方法的应用使幻灯片之间的色差降低了9.8，而线性判别分类器的分类性能提高了16.5％。配对t检验统计分析的结果进一步表明，在实施染色校正时，载玻片之间的CIELAB色彩差异和分类器性能的改善显着，在P <0.001时显着。
The stained colors of the tissue components are popularly used as features for image analysis. However, variations in the staining condition of the histology slides prompt variations to the color distribution of the stained tissue samples which could impact the accuracy of the analysis. In this paper, we present a method to correct the staining condition of a histology image. In the method, a look-up table (LUT) based on the dye amounts absorbed by the sample is built. The LUT can be built when either (i) the source and reference staining conditions are specified or (ii) when the user simply wants to recreate his/her preferred staining condition without specifying any reference slide. The effectiveness of the present method was evaluated in two aspects: (i) CIELAB color difference of nuclei, cytoplasm, and red blood cells, between the ten different slides of liver tissue, and (ii) classification of the different tissue components. Application of the present staining correction method reduced the color difference between the slides by an average factor of 9.8 and the classification performance of a linear discriminant classifier improved by 16.5 % on the average. Results of the paired t test statistical analysis further showed that the reduction in the CIELAB color difference between the slides and the improvement in the classifier's performance when staining correction was implemented is significant at p < 0.001.