Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and progression. Distinguishing stroma from epithelial tissues is critically important for spatial characterization of the tumor microenvironment. Here, we propose BrcaSeg, an image analysis pipeline based on a convolutional neural network (CNN) model to classify epithelial and stromal regions in whole-slide hematoxylin and eosin (H&E) stained histopathological images. The CNN model is trained using well-annotated breast cancer tissue microarrays and validated with images from The Cancer Genome Atlas (TCGA) Program. BrcaSeg achieves a classification accuracy of 91.02%, which outperforms other state-of-the-art methods. Using this model, we generate pixel-level epithelial/stromal tissue maps for 1000 TCGA breast cancer slide images that are paired with gene expression data. We subsequently estimate the epithelial and stromal ratios and perform correlation analysis to model the relationship between gene expression and tissue ratios. Gene Ontology (GO) enrichment analyses of genes that are highly correlated with tissue ratios suggest that the same tissue is associated with similar biological processes in different breast cancer subtypes, whereas each subtype also has its own idiosyncratic biological processes governing the development of these tissues. Taken all together, our approach can lead to new insights in exploring relationships between image-based phenotypes and their underlying genomic events and biological processes for all types of solid tumors. BrcaSeg can be accessed at https://github.com/Serian1992/ImgBio.

上皮和基质组织是肿瘤微环境的组成部分，在肿瘤的发生和发展中起主要作用。区分基质和上皮组织对于肿瘤微环境的空间表征至关重要。在这里，我们提出了 BrcaSeg，这是一种基于卷积神经网络 (CNN) 模型的图像分析管道，用于对全载玻片苏木精和伊红 (H&E) 染色的组织病理学图像中的上皮和基质区域进行分类。CNN 模型使用注释良好的乳腺癌组织微阵列进行训练，并使用来自癌症基因组图谱 (TCGA) 计划的图像进行验证。BrcaSeg达到 91.02% 的分类准确率，优于其他最先进的方法。使用该模型，我们为 1000 张与基因表达数据配对的 TCGA 乳腺癌幻灯片图像生成像素级上皮/基质组织图。我们随后估计上皮和基质比率并进行相关分析以模拟基因表达和组织比率之间的关系。与组织比率高度相关的基因的基因本体论 (GO) 富集分析表明，相同的组织与不同乳腺癌亚型的相似生物学过程相关，而每个亚型也有其自身的特殊生物学过程来控制这些组织的发育。综合起来，BrcaSeg可在 https://github.com/Serian1992/ImgBio 访问。