为高度复用的组织图像提供灵活、稳健的细胞类型标注

Huangqingbo Sun, Shiqiu Yu, Anna Martinez Casals, Anna Bäckström, Yuxin Lu, Cecilia Lindskog, Emma Lundberg, Robert F. Murphy
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引用次数: 0

摘要

在高度复用的图像中识别细胞类型对于了解组织的空间组织至关重要。目前的细胞类型标注方法通常依赖于大量的参考图像和人工调整。在这项工作中,我们提出了一种名为 "基于强大图像的细胞注释器"(RIBCA)的工具,无需额外的模型训练或人工干预,就能对具有各种抗体面板的图像进行准确、自动、无偏见和精细的细胞类型注释。我们的工具已成功注释了 100 多万个细胞,揭示了 40 多种不同人体组织中各种细胞类型的空间组织。该工具是开源的,采用模块化设计,可轻松扩展到其他细胞类型。
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Flexible and robust cell type annotation for highly multiplexed tissue images
Identifying cell types in highly multiplexed images is essential for understanding tissue spatial organization. Current cell type annotation methods often rely on extensive reference images and manual adjustments. In this work, we present a tool, Robust Image-Based Cell Annotator (RIBCA), that enables accurate, automated, unbiased, and fine-grained cell type annotation for images with a wide range of antibody panels, without requiring additional model training or human intervention. Our tool has successfully annotated over 1 million cells, revealing the spatial organization of various cell types across more than 40 different human tissues. It is open-source and features a modular design, allowing for easy extension to additional cell types.
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