Immunocto:一个庞大的免疫细胞数据库,自动生成用于组织病理学
Mikaë El Simard1, Zhuoyan Shen1, Konstantin Bräutigam2
1Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
Medical image analysis
|December 16, 2025
概括
我们开发了一个新的工作流程,自动创建Immunocto,这是从癌症组织图像中生成的免疫细胞的大数据库. 该数据库有助于研究瘤免疫微环境 (TIME) 并预测治疗反应.
科学领域:
- 计算病理学计算病理学
- 癌症免疫学 癌症免疫学
- 生物信息学是一种生物信息学.
背景情况:
- 研究瘤免疫微环境 (TIME) 对癌症预后和免疫治疗反应至关重要.
- 传统的时间分析方法是劳动密集型的,需要专门的专业知识.
- 组织病理学图像的自动化分析为 TIME 特性提供了一个可扩展的解决方案.
研究的目的:
- 开发一个自动化工作流程,从H&E和多重免疫光 (IF) 染色组织部分生成单细胞轮和标签.
- 创建Immunocto,一个大规模的,公开可用的人类细胞数据库,用于TIME研究.
- 为了证明Immunocto在训练免疫细胞检测深度学习模型中的实用性.
主要方法:
- 利用分段任何模型 (SAM) 进行自动细胞细分和标签.
- 组合H&E染色幻灯片与多重IF标记器用于双染色组织段.
- 创建了一个超过68万个细胞的数据库,包括220万个免疫细胞在四个亚型.
主要成果:
- 创建了Immunocto,一个庞大的数据库,包含6,848,454个细胞,其中有2,282,818个免疫细胞 (CD4 +,CD8 +,CD20 +,巨细胞).
- 每个单元格条目包括一个64x64像素的H&E图像,核面具和标签.
- 在Immunocto上训练的深度学习模型在淋巴细胞检测方面取得了最先进的性能.
结论:
- 开发的工作流程能够从例行组织病理学幻灯片中强大,自动生成单细胞数据.
- Immunocto为计算病理学提供了宝贵的资源,促进了TIME研究和模型培训.
- 该方法强调了整合匹配的H&E和IF数据的好处,以推进癌症研究.
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