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

• Type of Material: Article
• Authors: Alice Shen, University of California, San Diego; Fusheng Wang, Stony Brook University; Saptarshi Paul, Georgia State University; Divya Bhuvanapalli, Georgia State University; Jacob Alayof, Princeton University; Alton B Farris III, Emory University; George Teodoro, University of Brasilia; Daniel J. Brat, Northwestern University; Jun Kong, Emory University
• Language: English
• Date: 2022-11-09
• Publisher: IOP Publishing
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2022 Institute of Physics and Engineering in Medicine
• Final Published Version (URL): https://doi.org/10.1088/1361-6560/ac8fde
• Title of Journal or Parent Work: Physics in Medicine & Biology
• Volume: 67
• Issue: 22
• Start Page: 10.1088
• Grant/Funding Information: This research was supported by grants from National Institutes of Health U01CA242936, R01EY028450, R01CA214928, R01CA247905, National Science Foundation ACI 1443054 and IIS 1350885, and CNPq.
• Keywords: human tumor atlas-oriented studies; tissue investigation tools; Digital Pathology Laboratory
• Research Categories: Biology, Bioinformatics; Health Sciences, Pathology

Abstract

Objective: 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 (WSI) visualization, annotation, analysis, and sharing via web browsers. Approach: 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. Main results: 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. Significance: 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.