使用具有弱配对对的循环一致生成对抗网络进行基于光子吸收遥感技术的虚拟组织学
Virtual Histology with Photon Absorption Remote Sensing using a Cycle-Consistent Generative Adversarial Network with Weakly Registered Pairs
AI总结：
本研究提出一种基于无标记PARS显微镜的虚拟组织化学染色方法，结合Noise2Void去噪CNN、机械扫描误差校正算法及CycleGAN架构，生成与金标准H&E染色质量相当的虚拟染色图像，为标准组织病理学提供无标记替代方案。
摘要：
现代组织病理学依赖于对经过组织化学技术染色的薄组织切片进行显微镜检查，通常使用明场或荧光显微镜。然而，样本染色会永久改变其化学和结构，这意味着必须为每种所需的染色对比度单独制备组织切片。这不仅消耗宝贵的组织样本，还会延误必要的诊断时间线。在这项工作中，利用无标记的光子吸收遥感（PARS）显微镜开发了虚拟组织化学染色。我们提出了一种方法，可以生成与金标准苏木精和伊红（H&E）染色无法区分的虚拟染色组织学图像。首先，在未染色的组织标本中直接捕获PARS无标记紫外吸收图像。然后对辐射和非辐射吸收图像进行预处理，并通过所提出的途径进行虚拟染色。预处理流程包含一个自监督的Noise2Void去噪卷积神经网络（CNN），以及一种用于像素级机械扫描误差校正的新颖算法。这些进展显著增强了亚微米级组织结构的恢复，例如核仁位置和染色质分布。最后，我们使用循环一致生成对抗网络CycleGAN架构对预处理后的PARS数据进行虚拟染色。虚拟染色被应用于恶性人类皮肤和乳腺组织样本的未染色薄切片。该方法揭示了具有临床相关性的细节，其对比度和质量与金标准H&E染色图像相当。这项工作是将无标记显微镜作为标准组织病理学技术替代方案部署的关键一步。
英文摘要：
Modern histopathology relies on the microscopic examination of thin tissue sections stained with histochemical techniques, typically using brightfield or fluorescence microscopy. However, the staining of samples can permanently alter their chemistry and structure, meaning an individual tissue section must be prepared for each desired staining contrast. This not only consumes valuable tissue samples but also introduces delays in essential diagnostic timelines. In this work, virtual histochemical staining is developed using label-free photon absorption remote sensing (PARS) microscopy. We present a method that generates virtually stained histology images that are indistinguishable from the gold standard hematoxylin and eosin (H&E) staining. First, PARS label-free ultraviolet absorption images are captured directly within unstained tissue specimens. The radiative and non-radiative absorption images are then preprocessed, and virtually stained through the presented pathway. The preprocessing pipeline features a self-supervised Noise2Void denoising convolutional neural network (CNN) as well as a novel algorithm for pixel-level mechanical scanning error correction. These developments significantly enhance the recovery of sub-micron tissue structures, such as nucleoli location and chromatin distribution. Finally, we used a cycle-consistent generative adversarial network CycleGAN architecture to virtually stain the preprocessed PARS data. Virtual staining is applied to thin unstained sections of malignant human skin and breast tissue samples. Clinically relevant details are revealed, with comparable contrast and quality to gold standard H&E-stained images. This work represents a crucial step to deploying label-free microscopy as an alternative to standard histopathology techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
