Histological staining is a vital step in diagnosing various diseases and has been used for more than a century to provide contrast in tissue sections, rendering the tissue constituents visible for microscopic analysis by medical experts. However, this process is time consuming, labour intensive, expensive and destructive to the specimen. Recently, the ability to virtually stain unlabelled 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 that generates virtually stained images using label-free tissue images, in which 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 as its input: (1) autofluorescence images of the label-free tissue sample and (2) a "digital staining matrix", which represents the desired microscopic map of the different stains to be virtually generated in 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 unlabelled kidney tissue sections to generate micro-structured combinations of haematoxylin and eosin (H&E), Jones' silver stain, and Masson's trichrome stain. Using a single network, this approach multiplexes the virtual staining of label-free tissue images with multiple types of stains and paves the way for synthesizing new digital histological stains that can be created in the same tissue cross section, which is currently not feasible with standard histochemical staining methods.

中文翻译：

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使用无标签组织的微结构化和多重虚拟染色对组织学染色进行数字合成。

组织学染色是诊断各种疾病的重要步骤，并且已经使用了一个多世纪，以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，该过程费时，费力，昂贵并且对样品具有破坏性。最近，已经使用组织染色特异性深层神经网络证明了对未标记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，我们介绍了一个新的基于深度学习的框架，该框架使用无标签的组织图像生成虚拟染色的图像，其中根据用户定义的微结构图将不同的污渍合并。此方法使用单个深度神经网络，该网络接收两种不同的信息源作为输入：（1）无标记组织样品的自发荧光图像和（2）“数字染色矩阵”，它表示要在同一组织切片中虚拟生成的不同染色剂的所需显微图。该数字染色矩阵还用于虚拟混合现有染色，以数字方式合成新的组织学染色。我们使用未标记的肾脏组织切片训练并盲目测试了该虚拟染色网络，以生成苏木精和曙红（H＆E），琼斯银染和马森三色印染的微结构组合。使用单一网络，这种方法可以将无标记组织图像的虚拟染色与多种类型的染色进行复用，并为合成可以在同一组织横截面中产生的新的数字组织学染色铺平道路，