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虚拟组织学染色作为延长脏细分跨染色的工具

James Denholm¹, Azam Hamidinekoo², Nikolay Burlutskiy²

¹Department of Radiology, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom; Integrated Bioanalysis, Clinical Pharmacology and Safety Sciences (CPSS), AstraZeneca R&D, Cambridge, United Kingdom.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc

|July 25, 2025

概括

这项研究引入了虚拟组织学染色,以数字地将脏组织图像从血素和素 (H&E) 到周期性酸-Schiff (PAS) 恢复. 这种方法有助于在组织病理学中进行深度学习分析,克服各种染色的挑战.

科学领域:

数字病理学数字病理学
计算性组织病理学
人工智能在医学中的应用

背景情况:

临床上使用各种组织学染料使得对整片图像的深度学习分析变得复杂.
血素和欧 (H&E) 染色是常见的,但其他染色物,如周期性酸-Schiff (PAS) 对于特定的诊断至关重要.

研究的目的:

开发和验证一种in silico方法,使H&E染色脏组织几乎恢复到PAS.
通过使用单一的H&E染色,使脏组织病理学具有染色特异性的深度学习工具.

主要方法:

在一个大型的英国脏数据集上训练了循环一致的生成对抗神经网络.
一个虚拟染色模型从H&E推断出PAS,其现实性被病理学家评估.
在多个数据集上训练了一种球细分模型,并应用于虚拟染色图像.

主要成果:

病理学家无法可靠地区分虚拟PAS染色与真实PAS染色 (准确率为52.5%和75.8%).
虚拟染色方法在未见的H&E图像上成功推断出PAS染色.
特定于PAS的球细分模型在几乎染色的图像上表现得很好.

结论:

虚拟染色提供了一种可行的解决方案,以克服脏组织病理学中染色特异性深度学习的局限性.
这种技术通过从单个H&E幻灯片中实现多斑点分析来增强数字病理学的实用性.
进一步的研究可以扩展这种方法,以结合额外的组织学染色.

关键词:

国家统一脏翻译研究企业.慢性脏疾病慢性脏疾病计算病理学计算病理学生成型的人工智能 (GAI)脏病理学脏病理学虚拟染色是一种虚拟染色.

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