标题： 基于三维表面对称定量相位成像和虚拟苏木精和伊红染色的无标签和载玻片组织病理学 显示英文标题

标题： Label- and slide-free tissue histology using 3D epi-mode quantitative phase imaging and virtual H&E staining

摘要： 组织活检的组织学染色，特别是苏木精和伊红（H&E）染色，是疾病诊断和组织全面临床评估的标准。然而，该过程繁琐且耗时，常常限制了其在关键应用中的使用，例如手术边缘评估。为了解决这些挑战，我们结合了一种新兴的三维定量相位成像技术——称为定量斜向背光显微镜（qOBM），与一种无监督生成对抗网络管道，以将未经处理的厚组织的qOBM相位图像（即无标签和无载玻片）映射到虚拟染色的H&E类似（vH&E）图像。我们证明，这种方法使用小鼠肝脏、大鼠胶质肉瘤和人类胶质瘤的新鲜组织样本，能够以亚细胞细节实现高保真的H&E转换。我们还展示了该框架可以直接启用其他功能，例如体积成像的H&E类似对比度。通过用神经网络分类器验证vH&E图像的质量和保真度，该分类器训练于真实的H&E图像，并在虚拟H&E图像上进行测试，以及神经病理学家的用户研究。鉴于其简单且低成本的实现方式，并能够在体内提供实时反馈，这种基于深度学习的qOBM方法可以为组织病理学启用新的工作流程，有可能在癌症筛查、检测、治疗指导等方面显著节省时间和成本。 显示英文摘要

摘要： Histological staining of tissue biopsies, especially hematoxylin and eosin (H&E) staining, serves as the benchmark for disease diagnosis and comprehensive clinical assessment of tissue. However, the process is laborious and time-consuming, often limiting its usage in crucial applications such as surgical margin assessment. To address these challenges, we combine an emerging 3D quantitative phase imaging technology, termed quantitative oblique back illumination microscopy (qOBM), with an unsupervised generative adversarial network pipeline to map qOBM phase images of unaltered thick tissues (i.e., label- and slide-free) to virtually stained H&E-like (vH&E) images. We demonstrate that the approach achieves high-fidelity conversions to H&E with subcellular detail using fresh tissue specimens from mouse liver, rat gliosarcoma, and human gliomas. We also show that the framework directly enables additional capabilities such as H&E-like contrast for volumetric imaging. The quality and fidelity of the vH&E images are validated using both a neural network classifier trained on real H&E images and tested on virtual H&E images, and a user study with neuropathologists. Given its simple and low-cost embodiment and ability to provide real-time feedback in vivo, this deep learning-enabled qOBM approach could enable new workflows for histopathology with the potential to significantly save time, labor, and costs in cancer screening, detection, treatment guidance, and more.