Tam Allison, Barker Jocelyn, Rubin Daniel
Stanford Institutes of Medical Research Program, Stanford University School of Medicine, Stanford, California 94305.
Department of Radiology, Stanford University School of Medicine, Stanford, California 94305.
Med Phys. 2016 Jan;43(1):528. doi: 10.1118/1.4939130.
With the advent of digital slide scanning technologies and the potential proliferation of large repositories of digital pathology images, many research studies can leverage these data for biomedical discovery and to develop clinical applications. However, quantitative analysis of digital pathology images is impeded by batch effects generated by varied staining protocols and staining conditions of pathological slides.
To overcome this problem, this paper proposes a novel, fully automated stain normalization method to reduce batch effects and thus aid research in digital pathology applications. Their method, intensity centering and histogram equalization (ICHE), normalizes a diverse set of pathology images by first scaling the centroids of the intensity histograms to a common point and then applying a modified version of contrast-limited adaptive histogram equalization. Normalization was performed on two datasets of digitized hematoxylin and eosin (H&E) slides of different tissue slices from the same lung tumor, and one immunohistochemistry dataset of digitized slides created by restaining one of the H&E datasets.
The ICHE method was evaluated based on image intensity values, quantitative features, and the effect on downstream applications, such as a computer aided diagnosis. For comparison, three methods from the literature were reimplemented and evaluated using the same criteria. The authors found that ICHE not only improved performance compared with un-normalized images, but in most cases showed improvement compared with previous methods for correcting batch effects in the literature.
ICHE may be a useful preprocessing step a digital pathology image processing pipeline.
随着数字切片扫描技术的出现以及数字病理图像大型存储库的潜在扩散，许多研究可以利用这些数据进行生物医学发现并开发临床应用。然而，数字病理图像的定量分析受到病理切片不同染色方案和染色条件所产生的批次效应的阻碍。
为克服这一问题，本文提出一种新颖的全自动染色归一化方法，以减少批次效应，从而有助于数字病理应用的研究。他们的方法，即强度居中与直方图均衡化（ICHE），通过首先将强度直方图的质心缩放到一个公共点，然后应用对比度受限自适应直方图均衡化的改进版本，对各种病理图像进行归一化。对来自同一肺肿瘤不同组织切片的数字化苏木精和伊红（H&E）切片的两个数据集，以及通过对其中一个H&E数据集重新染色创建的数字化切片的一个免疫组织化学数据集进行了归一化。
基于图像强度值、定量特征以及对下游应用（如计算机辅助诊断）的影响对ICHE方法进行了评估。为作比较，重新实现了文献中的三种方法，并使用相同标准进行评估。作者发现，ICHE不仅与未归一化的图像相比提高了性能，而且在大多数情况下与文献中以前用于校正批次效应的方法相比也有改进。
ICHE可能是数字病理图像处理流程中一个有用的预处理步骤。
