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皮肤组织的虚拟组织学染色使用ex vivo聚焦显微镜和深度学习

Mahmoud Bagheri^1,2, Alireza Ghanadan³, Mobin Saboohi¹

¹Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.

Journal of biomedical physics & engineering

|October 22, 2025

概括

这项研究引入了一种深度学习模型,用于创建类似于Confocal Microscopy (CM) 图像的Hematoxylin-and-Eosin (H&E) 图像. 这种虚拟染色方法加快了用于诊断诸如基底细胞癌 (BCC) 等疾病的组织分析.

科学领域:

数字病理学数字病理学
医疗成像医学成像
人工智能的人工智能

背景情况:

血素和氨酸 (H&E) 染色是组织诊断的黄金标准,但需要大量的劳动和时间.
同焦显微镜 (CM) 提供快速,高分辨率的成像与最小的样本准备.
解释CM图像可能比H&E染色图像更具挑战性.

研究的目的:

调整一个无监督的深度学习模型,从CM数据中生成类似H&E的图像.
为了评估虚拟H&E染色对基底细胞癌 (BCC) 诊断的有效性.
使用先进的成像技术简化病理组织分析.

主要方法:

一个无监督的CycleGAN框架被训练为虚拟染色CM图像,模拟H&E染料使用阿克里丁色染色.
使用对抗和循环一致性损失来确保准确的图像映射,而不会改变内容.
生成的虚拟H&E图像在质量和数量上与原始H&E图像进行了比较.

主要成果:

循环GAN模型成功地从CM数据中生成了类似H&E的图像.
虚拟的H&E图像与真实的H&E染色皮肤瘤截图有显著的定性和定量相似之处.
该方法被证明是有效的基底细胞癌的亚型和评估皮肤组织特征.

结论:

将CM与基于深度学习的虚拟染色集成为加速诊断工作流提供了一个有希望的方法.
这种技术减少了对传统,耗时的H&E染色程序的依赖.
虚拟染色提高了CM在病理诊断应用中的实用性.

关键词:

基底细胞癌 (Basal Cell Carcinoma) 是一种基底细胞癌.与焦点相对的焦点相对的深度学习 (Deep Learning) 是一种深度学习.显微镜的使用方法病理学病理学病理学

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