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一种用于多维病理全切片图像分析的集成式网络软件工具。

An integrative web-based software tool for multi-dimensional pathology whole-slide image analytics.

机构信息

School of Medicine, University of California at San Diego, San Diego, CA, United States of America.

Department of Computer Science, Stony Brook University, Stony Brook, NY, United States of America.

出版信息

Phys Med Biol. 2022 Nov 9;67(22). doi: 10.1088/1361-6560/ac8fde.

Abstract

In the era of precision medicine, human tumor atlas-oriented studies have been significantly facilitated by high-resolution, multi-modal tissue based microscopic pathology image analytics. To better support such tissue-based investigations, we have developed Digital Pathology Laboratory (DPLab), a publicly available web-based platform, to assist biomedical research groups, non-technical end users, and clinicians for pathology whole-slide image visualization, annotation, analysis, and sharing via web browsers.A major advancement of this work is the easy-to-follow methods to reconstruct three-dimension (3D) tissue image volumes by registering two-dimension (2D) whole-slide pathology images of serial tissue sections stained by hematoxylin and eosin (H&E), and immunohistochemistry (IHC). The integration of these serial slides stained by different methods provides cellular phenotype and pathophysiologic states in the context of a 3D tissue micro-environment. DPLab is hosted on a publicly accessible server and connected to a backend computational cluster for intensive image analysis computations, with results visualized, downloaded, and shared via a web interface.Equipped with an analysis toolbox of numerous image processing algorithms, DPLab supports continued integration of community-contributed algorithms and presents an effective solution to improve the accessibility and dissemination of image analysis algorithms by research communities.DPLab represents the first step in making next generation tissue investigation tools widely available to the research community, enabling and facilitating discovery of clinically relevant disease mechanisms in a digital 3D tissue space.

摘要

在精准医学时代,高分辨率、多模态的基于组织的微观病理学图像分析极大地促进了人类肿瘤图谱导向的研究。为了更好地支持这种基于组织的研究,我们开发了 Digital Pathology Laboratory(DPLab),这是一个公开可用的基于网络的平台,用于协助生物医学研究小组、非技术终端用户和临床医生通过网络浏览器进行病理全切片图像的可视化、注释、分析和共享。这项工作的一个主要进展是,通过注册苏木精和伊红(H&E)染色和免疫组织化学(IHC)染色的连续组织切片的二维全切片病理图像,轻松地遵循方法来重建三维(3D)组织图像体积。这些通过不同方法染色的连续切片的整合提供了在 3D 组织微环境中细胞表型和病理生理状态。DPLab 托管在一个公共访问服务器上,并连接到一个后端计算集群,用于密集的图像分析计算,结果通过网络界面可视化、下载和共享。配备了大量图像处理算法的分析工具箱,DPLab 支持社区贡献的算法的持续集成,并为研究社区提供了一种有效的解决方案,以提高图像分析算法的可访问性和传播。DPLab 代表了使下一代组织研究工具广泛提供给研究社区的第一步,使在数字 3D 组织空间中发现与临床相关的疾病机制成为可能并得到促进。

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本文引用的文献

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Orbit Image Analysis: An open-source whole slide image analysis tool.眼眶图像分析:一个开源的全玻片图像分析工具。
PLoS Comput Biol. 2020 Feb 5;16(2):e1007313. doi: 10.1371/journal.pcbi.1007313. eCollection 2020 Feb.
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DYNAMIC REGISTRATION FOR GIGAPIXEL SERIAL WHOLE SLIDE IMAGES.千兆像素级连续全切片图像的动态配准
Proc IEEE Int Symp Biomed Imaging. 2017 Apr;2017:424-428. doi: 10.1109/ISBI.2017.7950552. Epub 2017 Jun 19.

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