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.

翻译：组织活检的组织学染色，尤其是苏木精-伊红（H&E）染色，是疾病诊断和组织临床综合评估的金标准。然而，该过程繁琐耗时，常限制其在手术切缘评估等关键场景中的应用。为解决这些问题，我们将一种新兴的三维定量相位成像技术——定量倾斜背照显微术（qOBM），与无监督生成对抗网络管道相结合，将未经处理的厚组织（即无标记、无切片）的qOBM相位图像映射为虚拟染色的类H&E（vH&E）图像。我们证明，该方法利用小鼠肝脏、大鼠神经胶质肉瘤和人脑胶质瘤的新鲜组织标本，实现了亚细胞细节水平下向H&E图像的高保真转换。我们还表明，该框架可直接实现体成像等额外功能，如H&E对比度增强。通过基于真实H&E图像训练的神经网络分类器对虚拟H&E图像进行测试，以及神经病理学家的用户研究，验证了vH&E图像的质量和保真度。鉴于其简单、低成本的特点及在体内提供实时反馈的能力，这种深度学习赋能的qOBM方法有望开创组织病理学的新工作流程，在癌症筛查、检测、治疗指导等领域显著节省时间、人力和成本。