BACKGROUND We present an image dataset related to automated segmentation and counting of macrophages in diffuse large B-cell lymphoma (DLBCL) tissue sections. For the classification of DLBCL subtypes, as well as for providing a prognosis of the clinical outcome, the analysis of the tumor microenvironment and, particularly, of the different types and functions of tumor-associated macrophages is indispensable. Until now, however, most information about macrophages has been obtained either in a completely indirect way by gene expression profiling or by manual counts in immunohistochemically (IHC) fluorescence-stained tissue samples while automated recognition of single IHC stained macrophages remains a difficult task. In an accompanying publication, a reliable approach to this problem has been established, and a large set of related images has been generated and analyzed. RESULTS Provided image data comprise (i) fluorescence microscopy images of 44 multiple immunohistostained DLBCL tumor subregions, captured at 4 channels corresponding to CD14, CD163, Pax5, and DAPI; (ii) "cartoon-like" total variation-filtered versions of these images, generated by Rudin-Osher-Fatemi denoising; (iii) an automatically generated mask of the evaluation subregion, based on information from the DAPI channel; and (iv) automatically generated segmentation masks for macrophages (using information from CD14 and CD163 channels), B-cells (using information from Pax5 channel), and all cell nuclei (using information from DAPI channel). CONCLUSIONS A large set of IHC stained DLBCL specimens is provided together with segmentation masks for different cell populations generated by a reference method for automated image analysis, thus featuring considerable reuse potential.

中文翻译：

---

与免疫染色的淋巴瘤组织样本中自动巨噬细胞检测有关的图像数据集。

背景技术我们提出了与弥散性大B细胞淋巴瘤（DLBCL）组织切片中的巨噬细胞自动分割和计数有关的图像数据集。为了对DLBCL亚型进行分类以及提供临床预后，对肿瘤微环境，尤其是与肿瘤相关的巨噬细胞的不同类型和功能的分析是必不可少的。但是，到目前为止，有关巨噬细胞的大多数信息已经通过基因表达谱分析或通过免疫组织化学（IHC）荧光染色的组织样品中的人工计数以完全间接的方式获得，而单个IHC染色的巨噬细胞的自动识别仍然是一项艰巨的任务。在随附的出版物中，已经建立了解决此问题的可靠方法，并且已经生成并分析了大量相关图像。结果提供的图像数据包括（i）44个免疫组化的多个DLBCL肿瘤亚区域的荧光显微镜图像，分别在与CD14，CD163，Pax5和DAPI对应的4个通道处捕获；（ii）通过Rudin-Osher-Fatemi去噪生成的这些图像的“类似卡通”的总变化过滤版本；（iii）根据来自DAPI通道的信息，自动生成评估子区域的掩码；（iv）自动为巨噬细胞（使用来自CD14和CD163通道的信息），B细胞（使用来自Pax5通道的信息）和所有细胞核（使用来自DAPI通道的信息）生成分段掩码。