arXiv 2306.005482023-06-01eess.IVcs.CVcs.LGphysics.med-phq-bio.QM

使用3D落射模式定量相位成像和虚拟H&E染色的无标记、无玻片组织组织学

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

University of California, Davis（加利福尼亚大学戴维斯分校）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）；Emory University School of Medicine（埃默里大学医学院）；UC Davis Health（加州大学戴维斯分校医疗系统）

Tanishq Mathew Abraham, Paloma Casteleiro Costa, Caroline Filan, Zhe Guang, Zhaobin Zhang, Stewart Neill, Jeffrey J. Olson, Richard Levenson, Francisco E. Robles

备注：: 30 pages, 9 main figures, 1 table, 5 supplementary figures

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AI总结：

本文提出结合3D qOBM定量相位成像与无监督GAN的无标记、无玻片虚拟H&E染色框架，并在多种新鲜组织和病理专家评估中验证其高保真诊断价值。

摘要：

组织活检的组织学染色，尤其是苏木精和伊红（H&E）染色，是疾病诊断和组织综合临床评估的基准。然而，该过程费力且耗时，常常限制其在切缘评估等关键应用中的使用。为应对这些挑战，我们将一种新兴的3D定量相位成像技术——称为定量斜向背光照明显微镜（qOBM）——与无监督生成对抗网络流程相结合，把未经改变的厚组织（即无标记且无玻片）的qOBM相位图像映射为虚拟染色的类H&E（vH&E）图像。我们使用来自小鼠肝脏、大鼠胶质肉瘤和人类胶质瘤的新鲜组织标本证明，该方法实现了向H&E的高保真转换，并具有亚细胞细节。我们还表明，该框架可直接实现额外能力，例如用于体积成像的类H&E对比度。vH&E图像的质量和保真度通过两种方式得到验证：一是在真实H&E图像上训练并在虚拟H&E图像上测试的神经网络分类器，二是由神经病理学家参与的用户研究。鉴于其简单、低成本的实现方式以及在体内提供实时反馈的能力，这种由深度学习赋能的qOBM方法可促成新的组织病理学工作流程，并有可能在癌症筛查、检测、治疗指导等方面显著节省时间、人力和成本。

英文摘要：

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.

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