The tumor microenvironment is an interacting heterogeneous collection of cancer cells, resident as well as infiltrating host cells, secreted factors, and extracellular matrix proteins. With the growing importance of immunotherapies, it has become crucial to be able to characterize the composition and the functional orientation of the microenvironment. The development of novel computational image analysis methodologies may enable the robust quantification and localization of immune and related biomarker-expressing cells within the microenvironment. The aim of the review is to concisely highlight a selection of current and significant contributions pertinent to methodological advances coupled with biomedical or translational applications. A further aim is to concisely present computational advances that, to our knowledge, have currently very limited use for the assessment of the microenvironment but have the potential to enhance image analysis pipelines; on this basis, an example is shown for the detection and segmentation of cells of the microenvironment using a published pipeline and a public dataset. Finally, a general proposal is presented on the conceptual design of automation-optimized computational image analysis workflows in the biomedical and clinical domain.

肿瘤微环境是癌细胞、常驻和浸润宿主细胞、分泌因子和细胞外基质蛋白的相互作用的异质集合。随着免疫疗法的重要性日益增加，能够表征微环境的组成和功能方向变得至关重要。新的计算图像分析方法的发展可以实现微环境中免疫和相关生物标志物表达细胞的稳健量化和定位。审查的目的是简明扼要地突出选择与方法学进步以及生物医学或转化应用相关的当前和重要贡献。另一个目标是简明地介绍计算进展，据我们所知，目前用于微环境评估的用途非常有限，但有可能增强图像分析管道；在此基础上，展示了使用已发布的管道和公共数据集检测和分割微环境细胞的示例。最后，提出了关于生物医学和临床领域自动化优化计算图像分析工作流程的概念设计的一般建议。