Jen Kuang-Yu, Murali Leema Krishna, Lutnick Brendon, Ginley Brandon, Govind Darshana, Mori Hidetoshi, Gao Guofeng, Sarder Pinaki
Department of Pathology and Laboratory Medicine, University of California, Davis School of Medicine.
Department of Biomedical Engineering, SUNY Buffalo.
Proc SPIE Int Soc Opt Eng. 2021 Feb;11603. doi: 10.1117/12.2581795. Epub 2021 Feb 15.
With the rapid advancement in multiplex tissue staining, computer hardware, and machine learning, computationally-based tools are becoming indispensable for the evaluation of digital histopathology. Historically, standard histochemical staining methods such as hematoxylin and eosin, periodic acid-Schiff, and trichrome have been the gold standard for microscopic tissue evaluation by pathologists, and therefore brightfield microscopy images derived from such stains are primarily used for developing computational pathology tools. However, these histochemical stains are nonspecific in terms of highlighting structures and cell types. In contrast, immunohistochemical stains use antibodies to specifically detect and quantify proteins, which can be used to specifically highlight structures and cell types of interest. Traditionally, such immunofluorescence-based methods are only able to simultaneously stain a limited number of target proteins/antigens, typically up to three channels. Fluorescence-based multiplex immunohistochemistry (mIHC) is a new technology that enables simultaneous localization and quantification of numerous proteins/antigens, allowing for the possibility to detect a wide range of histologic structures and cell types within tissue. However, this method is limited by cost, specialized equipment, technical expertise, and time. In this study, we implemented a deep learning-based pipeline to synthetically generate mIHC images from brightfield images of tissue slides-stained with routinely used histochemical stains, in particular PAS. Our tool was trained using fluorescence-based mIHC images as the ground-truth. The proposed pipeline offers high contrast detection of structures in brightfield imaged tissue sections stained with standard histochemical stains. We demonstrate the performance of our pipeline by computationally detecting multiple compartments in kidney biopsies, including cell nuclei, collagen/fibrosis, distal tubules, proximal tubules, endothelial cells, and leukocytes, from PAS-stained tissue sections. Our work can be extended for other histologic structures and tissue types and can be used as a basis for future automated annotation of histologic structures and cell types without the added cost of actually generating mIHC slides.
随着多重组织染色、计算机硬件和机器学习的迅速发展，基于计算的工具对于数字组织病理学评估变得不可或缺。从历史上看，苏木精和伊红、过碘酸 - 希夫染色和三色染色等标准组织化学染色方法一直是病理学家进行微观组织评估的金标准，因此源自此类染色的明场显微镜图像主要用于开发计算病理学工具。然而，这些组织化学染色在突出结构和细胞类型方面是非特异性的。相比之下，免疫组织化学染色使用抗体来特异性检测和定量蛋白质，可用于特异性突出感兴趣的结构和细胞类型。传统上，这种基于免疫荧光的方法只能同时对有限数量的靶蛋白/抗原进行染色，通常最多三个通道。基于荧光的多重免疫组织化学（mIHC）是一项新技术，能够同时对多种蛋白质/抗原进行定位和定量，从而有可能检测组织内广泛的组织结构和细胞类型。然而，该方法受到成本、专业设备、技术专长和时间的限制。在本研究中，我们实施了一种基于深度学习的流程，从用常规使用的组织化学染色（特别是PAS）染色的组织切片的明场图像中合成生成mIHC图像。我们的工具使用基于荧光的mIHC图像作为真值进行训练。所提出的流程在明场成像的、用标准组织化学染色的组织切片中提供了高对比度的结构检测。我们通过从PAS染色的组织切片中计算检测肾活检中的多个区域，包括细胞核、胶原蛋白/纤维化、远端小管、近端小管、内皮细胞和白细胞，来证明我们流程的性能。我们的工作可以扩展到其他组织结构和组织类型，并可作为未来对组织结构和细胞类型进行自动注释的基础，而无需实际生成mIHC玻片的额外成本。
