Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
Bioengineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
Nat Biomed Eng. 2019 Jun;3(6):466-477. doi: 10.1038/s41551-019-0362-y. Epub 2019 Mar 4.
The histological analysis of tissue samples, widely used for disease diagnosis, involves lengthy and laborious tissue preparation. Here, we show that a convolutional neural network trained using a generative adversarial-network model can transform wide-field autofluorescence images of unlabelled tissue sections into images that are equivalent to the bright-field images of histologically stained versions of the same samples. A blind comparison, by board-certified pathologists, of this virtual staining method and standard histological staining using microscopic images of human tissue sections of the salivary gland, thyroid, kidney, liver and lung, and involving different types of stain, showed no major discordances. The virtual-staining method bypasses the typically labour-intensive and costly histological staining procedures, and could be used as a blueprint for the virtual staining of tissue images acquired with other label-free imaging modalities.
组织样本的组织学分析广泛用于疾病诊断，涉及冗长而繁琐的组织准备。在这里，我们表明，使用生成对抗网络模型训练的卷积神经网络可以将未标记的组织切片的宽场自发荧光图像转换为与相同样本的组织学染色的明场图像等效的图像。通过董事会认证的病理学家对这种虚拟染色方法和使用唾液腺、甲状腺、肾脏、肝脏和肺的人体组织切片的显微镜图像进行的标准组织学染色的盲目比较显示没有重大差异。虚拟染色方法绕过了通常劳动强度大且成本高的组织学染色程序，可作为使用其他无标记成像方式获取的组织图像进行虚拟染色的蓝图。
