Histological staining is a vital step used to diagnose various diseases and has been used for more than a century to provide contrast to tissue sections, rendering the tissue constituents visible for microscopic analysis by medical experts. However, this process is time-consuming, labor-intensive, expensive and destructive to the specimen. Recently, the ability to virtually-stain unlabeled tissue sections, entirely avoiding the histochemical staining step, has been demonstrated using tissue-stain specific deep neural networks. Here, we present a new deep learning-based framework which generates virtually-stained images using label-free tissue, where different stains are merged following a micro-structure map defined by the user. This approach uses a single deep neural network that receives two different sources of information at its input: (1) autofluorescence images of the label-free tissue sample, and (2) a digital staining matrix which represents the desired microscopic map of different stains to be virtually generated at the same tissue section. This digital staining matrix is also used to virtually blend existing stains, digitally synthesizing new histological stains. We trained and blindly tested this virtual-staining network using unlabeled kidney tissue sections to generate micro-structured combinations of Hematoxylin and Eosin (H&E), Jones silver stain, and Masson's Trichrome stain. Using a single network, this approach multiplexes virtual staining of label-free tissue with multiple types of stains and paves the way for synthesizing new digital histological stains that can be created on the same tissue cross-section, which is currently not feasible with standard histochemical staining methods.

中文翻译：

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使用无标记组织的微结构和多重虚拟染色进行组织学染色的数字合成

组织学染色是用于诊断各种疾病的重要步骤，一个多世纪以来一直用于提供组织切片的对比，使组织成分可见，以便医学专家进行显微镜分析。然而，该过程耗时、劳动密集、昂贵且对样品具有破坏性。最近，已经使用组织染色特定的深层神经网络证明了对未标记的组织切片进行虚拟染色的能力，完全避免了组织化学染色步骤。在这里，我们提出了一个新的基于深度学习的框架，它使用无标签组织生成虚拟染色图像，其中不同的染色按照用户定义的微结构图进行合并。这种方法使用单个深度神经网络，在其输入处接收两个不同的信息源：(1) 无标记组织样本的自发荧光图像，以及 (2) 数字染色矩阵，表示在同一组织切片上虚拟生成的不同染色的所需显微图。这种数字染色基质还用于虚拟混合现有染色，以数字方式合成新的组织染色。我们使用未标记的肾组织切片训练并盲目测试了这个虚拟染色网络，以生成苏木精和曙红 (H&E)、琼斯银染色和马森三色染色的微结构组合。使用单个网络，这种方法将无标记组织的虚拟染色与多种类型的染色进行多重化，并为合成可在同一组织横截面上创建的新数字组织学染色铺平了道路，