Abstract：
Tissue structures, phenotypes, and pathology are routinely investigated based on histology. This includes chemically staining the transparent tissue sections to make them visible to the human eye. Although chemical staining is fast and routine, it permanently alters the tissue and often consumes hazardous reagents. On the other hand, on using adjacent tissue sections for combined measurements, the cell-wise resolution is lost owing to sections representing different parts of the tissue. Hence, techniques providing visual information of the basic tissue structure enabling additional measurements from the exact same tissue section are required. Here we tested unstained tissue imaging for the development of computational hematoxylin and eosin (HE) staining. We used unsupervised deep learning (CycleGAN) and whole slide images of prostate tissue sections to compare the performance of imaging tissue in paraffin, as deparaffinized in air, and as deparaffinized in mounting medium with section thicknesses varying between 3 and 20 μm. We showed that although thicker sections increase the information content of tissue structures in the images, thinner sections generally perform better in providing information that can be reproduced in virtual staining. According to our results, tissue imaged in paraffin and as deparaffinized provides a good overall representation of the tissue for virtually HE-stained images. Further, using a pix2pix model, we showed that the reproduction of overall tissue histology can be clearly improved with image-to-image translation using supervised learning and pixel-wise ground truth. We also showed that virtual HE staining can be used for various tissues and used with both 20× and 40× imaging magnifications. Although the performance and methods of virtual staining need further development, our study provides evidence of the feasibility of whole slide unstained microscopy as a fast, cheap, and feasible approach to producing virtual staining of tissue histology while sparing the exact same tissue section ready for subsequent utilization with follow-up methods at single-cell resolution.
摘要：
组织结构,表型，和病理学的常规调查基于组织学。这包括对透明组织切片进行化学染色以使其对人眼可见。虽然化学染色是快速和常规的，它会永久改变组织，并经常消耗危险试剂。另一方面,在使用相邻的组织切片进行组合测量时，由于代表组织的不同部分的切片，细胞分辨率丢失。因此，需要提供基本组织结构的视觉信息的技术,使得能够从完全相同的组织切片进行额外的测量。在这里，我们测试了未染色的组织成像的计算苏木精和伊红（HE）染色的发展。我们使用无监督深度学习（CycleGAN）和前列腺组织切片的整个幻灯片图像来比较石蜡成像组织的性能。像在空气中脱蜡一样，并且在截面厚度在3至20μm之间变化的安装介质中脱蜡。我们表明，尽管较厚的切片增加了图像中组织结构的信息含量，较薄的切片通常在提供可以在虚拟染色中再现的信息方面表现更好。根据我们的结果,在石蜡中成像并脱蜡的组织为几乎HE染色的图像提供了组织的良好总体表示。Further,使用pix2pix模型，我们表明，使用监督学习和像素式地面实况，通过图像到图像的翻译，可以明显改善整体组织组织学的再现。我们还表明，虚拟HE染色可用于各种组织，并可同时使用20倍和40倍成像放大倍数。虽然虚拟染色的性能和方法需要进一步发展，我们的研究提供了整个载玻片未染色显微镜作为一种快速，便宜,和可行的方法，以产生组织组织学的虚拟染色，同时保留完全相同的组织切片，以备后续使用单细胞分辨率的后续方法使用。
