Immunocto：为组织病理学自动生成的大规模免疫细胞数据库
Immunocto: a massive immune cell database auto-generated for histopathology
AI总结：
本文提出Immunocto，一个通过H&E与多重IF染色结合Segment Anything Model自动生成的包含684万细胞（含228万免疫细胞）的数据库，用于训练深度学习模型以研究肿瘤免疫微环境，并在淋巴细胞检测上达到最先进性能。
摘要：
随着免疫治疗等新型癌症治疗方法的出现，研究肿瘤免疫微环境（TIME）对于判断预后和理解对治疗药物的潜在反应至关重要。表征TIME的一个关键方法可能是将（1）常规组织病理学检查中获得的苏木精-伊红（H&E）染色组织切片的数字化高分辨率光学图像与（2）自动化免疫细胞检测和分类方法相结合。在这项工作中，我们介绍了一种工作流程，可从H&E和多重免疫荧光（IF）标记双重染色的组织切片中自动生成稳健的单细胞轮廓和标签。该方法利用了Segment Anything Model，与现有的单细胞数据库相比，所需的人工干预极少。利用这种方法，我们创建了Immunocto，一个大规模、数百万级自动生成的数据库，包含6,848,454个人类细胞和物体，其中包括2,282,818个免疫细胞，分布在4个亚型中：CD4$^+$ T细胞淋巴细胞、CD8$^+$ T细胞淋巴细胞、CD20$^+$ B细胞淋巴细胞和CD68$^+$/CD163$^+$巨噬细胞。对于每个细胞，我们提供一张在$\times$40倍放大下的64$\times$64像素$^2$的H&E图像，以及细胞核的二值掩膜和标签。该数据库已公开，可用于训练模型以在常规H&E切片上研究TIME。我们表明，在Immunocto上训练的深度学习模型在淋巴细胞检测方面达到了最先进的性能。该方法展示了利用匹配的H&E和IF数据为计算病理学应用生成稳健数据库的优势。
英文摘要：
With the advent of novel cancer treatment options such as immunotherapy, studying the tumour immune micro-environment (TIME) is crucial to inform on prognosis and understand potential response to therapeutic agents. A key approach to characterising the TIME may be through combining (1) digitised microscopic high-resolution optical images of hematoxylin and eosin (H&E) stained tissue sections obtained in routine histopathology examinations with (2) automated immune cell detection and classification methods. In this work, we introduce a workflow to automatically generate robust single cell contours and labels from dually stained tissue sections with H&E and multiplexed immunofluorescence (IF) markers. The approach harnesses the Segment Anything Model and requires minimal human intervention compared to existing single cell databases. With this methodology, we create Immunocto, a massive, multi-million automatically generated database of 6,848,454 human cells and objects, including 2,282,818 immune cells distributed across 4 subtypes: CD4$^+$ T cell lymphocytes, CD8$^+$ T cell lymphocytes, CD20$^+$ B cell lymphocytes, and CD68$^+$/CD163$^+$ macrophages. For each cell, we provide a 64$\times$64 pixels$^2$ H&E image at $\mathbf{40}\times$ magnification, along with a binary mask of the nucleus and a label. The database, which is made publicly available, can be used to train models to study the TIME on routine H&E slides. We show that deep learning models trained on Immunocto result in state-of-the-art performance for lymphocyte detection. The approach demonstrates the benefits of using matched H&E and IF data to generate robust databases for computational pathology applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
