New Colors for Histology: Optimized Bivariate Color Maps Increase Perceptual Contrast in Histological Images

BackgroundAccurate evaluation of immunostained histological images is required for reproducible research in many different areas and forms the basis of many clinical decisions. The quality and efficiency of histopathological evaluation is limited by the information content of a histological image, which is primarily encoded as perceivable contrast differences between objects in the image. However, the colors of chromogen and counterstain used for histological samples are not always optimally distinguishable, even under optimal conditions.Methods and ResultsIn this study, we present a method to extract the bivariate color map inherent in a given histological image and to retrospectively optimize this color map. We use a novel, unsupervised approach based on color deconvolution and principal component analysis to show that the commonly used blue and brown color hues in Hematoxylin—3,3’-Diaminobenzidine (DAB) images are poorly suited for human observers. We then demonstrate that it is possible to construct improved color maps according to objective criteria and that these color maps can be used to digitally re-stain histological images.ValidationTo validate whether this procedure improves distinguishability of objects and background in histological images, we re-stain phantom images and N = 596 large histological images of immunostained samples of human solid tumors. We show that perceptual contrast is improved by a factor of 2.56 in phantom images and up to a factor of 2.17 in sets of histological tumor images.ContextThus, we provide an objective and reliable approach to measure object distinguishability in a given histological image and to maximize visual information available to a human observer. This method could easily be incorporated in digital pathology image viewing systems to improve accuracy and efficiency in research and diagnostics.【背景】在诸多领域的可重复研究中，免疫染色组织学图像的精准评估是必要前提，同时也是众多临床决策的核心依据。组织病理学评估的质量与效率受限于组织学图像的信息承载量——这类图像的信息主要以图像内目标间可感知的对比度差异进行编码。然而，即便在最优观测条件下，组织样本所使用的显色剂与复染剂的色彩往往并非最优可区分组合。 【方法与结果】本研究提出一种方法，可从给定的组织学图像中提取其固有二元色彩映射，并对该色彩映射进行回溯性优化。我们采用一种基于颜色反卷积（color deconvolution）与主成分分析（principal component analysis）的新型无监督方法，证实了苏木精-3,3'-二氨基联苯胺（Hematoxylin—3,3’-Diaminobenzidine, DAB）图像中常用的蓝色与棕色色调，对人类观测者而言辨识度极差。随后我们证明，可依据客观标准构建优化后的色彩映射，并将其用于组织学图像的数字化复染。 【验证】为验证该流程是否可提升组织学图像中目标与背景的区分度，我们对体模图像（phantom images）以及N=596张人类实体瘤免疫染色样本的大型组织学图像进行了数字化复染。实验结果表明，体模图像的感知对比度提升了2.56倍，而实体瘤组织学图像集的感知对比度最高可提升2.17倍。 【研究意义】综上，本研究提出了一种客观可靠的方法，可量化评估给定组织学图像中的目标区分度，并最大化人类观测者可获取的视觉信息。该方法可便捷集成至数字病理（digital pathology）图像查看系统中，从而提升研究与临床诊断的准确性与效率。