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基于图像分析的结肠炎小鼠模型评分方法

Image Analysis-Based Approaches for Scoring Mouse Models of Colitis.

作者信息

Rogers R, Eastham-Anderson J, DeVoss J, Lesch J, Yan D, Xu M, Solon M, Hotzel K, Diehl L, Webster J D

机构信息

Department of Pathology, Genentech, South San Francisco, CA, USA.

Department of Translational Immunology, Genentech, South San Francisco, CA, USA.

出版信息

Vet Pathol. 2016 Jan;53(1):200-10. doi: 10.1177/0300985815579998. Epub 2015 Apr 23.

Abstract

Mouse models of inflammatory bowel disease are critical for basic and translational research that is advancing the understanding and treatment of this disease. Assessment of these mouse models frequently relies on histologic endpoints. In recent years, whole slide imaging and digital pathology-based image analysis platforms have become increasingly available for implementation into the pathology workflow. These automated image analysis approaches allow for nonbiased quantitative assessment of histologic endpoints. In this study, the authors sought to develop an image analysis workflow using a commercially available image analysis platform that requires minimal training in image analysis or programming, and this workflow was used to score 2 mouse models of colitis that are primarily characterized by immune cell infiltrates in the lamina propria. Although the software was unable to accurately and consistently segment hematoxylin and eosin-stained sections, automated quantification of CD3 immunolabeling resulted in strong correlations with the pathologist's score in all studies and allowed for the identification of 8 of the 9 differences among treatment groups that were identified by the pathologist. These results demonstrate not only the ability to incorporate solutions based on image analysis into the pathologist's workflow but also the importance of immunohistochemical or histochemical surrogates for the incorporation of image analysis in histologic assessments.

摘要

炎症性肠病的小鼠模型对于推进该疾病的理解和治疗的基础及转化研究至关重要。对这些小鼠模型的评估常常依赖于组织学终点。近年来,全玻片成像和基于数字病理学的图像分析平台越来越多地可用于纳入病理工作流程。这些自动化图像分析方法允许对组织学终点进行无偏倚的定量评估。在本研究中,作者试图使用一种商业可用的图像分析平台开发一种图像分析工作流程,该平台在图像分析或编程方面所需培训极少,并且此工作流程用于对2种结肠炎小鼠模型进行评分,这些模型的主要特征是固有层中有免疫细胞浸润。尽管该软件无法准确且一致地分割苏木精和伊红染色切片,但CD3免疫标记的自动定量在所有研究中都与病理学家的评分有很强的相关性,并能够识别出病理学家所确定的治疗组之间9个差异中的8个。这些结果不仅证明了将基于图像分析的解决方案纳入病理学家工作流程的能力,还证明了免疫组织化学或组织化学替代物对于在组织学评估中纳入图像分析的重要性。

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