自动细胞注释和分类在组织病理学上用于空间生物标志物发现
Zhe Li1, Seyed Hossein Mirjahanmardi1, Rasoul Sali1
1Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Nature communications
|July 7, 2025
概括
在H&E染色图像上的自动细胞注释使用多重免疫光学 (mIF) 进行精确的细胞分类. 这种方法可以发现用于精密瘤学的空间生物标志物.
科学领域:
- 计算病理学计算病理学
- 发现生物标志物的发现.
- 数字病理学数字病理学
背景情况:
- 用血素和色素 (H&E) 染色的组织病理学对于临床诊断至关重要.
- 对组织病理学的单细胞分析提供了对疾病和治疗反应的见解.
- 目前的方法依赖于低效且容易出错的人类注释.
研究的目的:
- 开发一种自动化的方法来对H&E染色图像进行细胞注释和分类.
- 创建一个高质量的注释数据集,用于训练深度学习模型.
- 研究免疫细胞空间相互作用与患者结果之间的联系.
主要方法:
- 多复合免疫光学 (mIF) 用于根据蛋白质标记物定义细胞类型.
- 在单个细胞层面上,H&E图像与mIF数据一起注册.
- 结合自主监督学习和域调整的深度学习模型被训练用于细胞分类.
主要成果:
- 一个包含1,127,252个单元的数据集使用mIF进行了注释.
- 深度学习模型在H&E图像上分类四种细胞类型时达到86%-89%的准确性.
- 细胞分类模型证明了对整个幻灯片图像的适用性.
- 瘤微环境中的免疫细胞的空间相互作用与患者的存活率和对免疫检查点抑制剂的反应有关.
结论:
- 开发的方法为标准组织病理学的单细胞分析提供了一个可扩展的方法.
- 这种技术可以促进用于精密瘤学的新型空间生物标志物的发现.
- 自动化的细胞注释克服了数字病理学中手动注释的局限性.
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