Deep Learning for Virtual Histological Staining of Bright-Field Microscopic Images of Unlabeled Carotid Artery Tissue
Deep Learning for Virtual Histological Staining of Bright-Field Microscopic Images of Unlabeled Carotid Artery Tissue
复制标题
未标记颈动脉组织明场显微图像虚拟组织学染色的深度学习
DOI：
10.1007/s11307-020-01508-6
复制
发表时间：
2020-06-08
影响因子：
3.1
通讯作者：
Tian, Jie
中科院分区：
文献类型：
--
作者：
Li, Dan;Hui, Hui;Tian, Jie
目的动脉组织标本的组织学分析是一种广泛应用于心血管疾病诊断和定量的方法。然而，可变和劳动密集型的组织染色程序阻碍了有效和信息丰富的组织学图像分析。在本研究中，我们开发了一种基于深度学习的方法，将未标记组织切片的明场显微镜图像转换为相同样品的组织学染色版本的等效明场图像。我们训练了一个卷积神经网络，使用条件生成对抗网络模型在未染色图像和组织学染色图像之间建立映射。结果经专业病理学家盲法鉴定，不同染色类型的大鼠颈动脉组织切片的虚拟染色和标准组织学染色图像无明显差异。颈动脉组织切片虚拟和组织学H&E染色定量显示，内膜厚度、内膜面积和中膜面积的相对误差分别小于1.6%、5.6%和12.7%。深度学习网络的训练时间为12.857 h，训练patch 1800个，epoch 200个。结论：这种虚拟染色方法显著减轻了典型的费力和耗时的组织学染色程序，并可与其他无标签显微成像方式相结合。
Purpose Histological analysis of artery tissue samples is a widely used method for diagnosis and quantification of cardiovascular diseases. However, the variable and labor-intensive tissue staining procedures hinder efficient and informative histological image analysis. Procedures In this study, we developed a deep learning-based method to transfer bright-field microscopic images of unlabeled tissue sections into equivalent bright-field images of histologically stained versions of the same samples. We trained a convolutional neural network to build maps between the unstained images and histologically stained images using a conditional generative adversarial network model. Results The results of a blind evaluation by board-certified pathologists illustrate that the virtual staining and standard histological staining images of rat carotid artery tissue sections and those involving different types of stains showed no major differences. Quantification of virtual and histological H&E staining in carotid artery tissue sections showed that the relative errors of intima thickness, intima area, and media area were lower than 1.6 %, 5.6 %, and 12.7 %, respectively. The training time of deep learning network was 12.857 h with 1800 training patches and 200 epoches. Conclusions This virtual staining method significantly mitigates the typically laborious and time-consuming histological staining procedures and could be augmented with other label-free microscopic imaging modalities.