Image microarrays (IMA): Digital pathology′s missing tool

Journal of Pathology Informatics. 2011;2(1):47-47 DOI 10.4103/2153-3539.86829


Journal Homepage

Journal Title: Journal of Pathology Informatics

ISSN: 2229-5089 (Print); 2153-3539 (Online)

Publisher: Wolters Kluwer Medknow Publications

Society/Institution: Association for Pathology Informatics

LCC Subject Category: Medicine: Medicine (General): Computer applications to medicine. Medical informatics | Medicine: Pathology

Country of publisher: India

Language of fulltext: English

Full-text formats available: PDF, HTML, ePUB



Jason Hipp
Jerome Cheng
Liron Pantanowitz
Stephen Hewitt
Yukako Yagi
James Monaco
Anant Madabhushi
Jaime Rodriguez-canales
Jeffrey Hanson
Sinchita Roy-Chowdhuri
Armando C Filie
Michael D Feldman
John E Tomaszewski
Natalie NC Shih
Victor Brodsky
Giuseppe Giaccone
Michael R Emmert-Buck
Ulysses J Balis


Peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 18 weeks


Abstract | Full Text

Introduction: The increasing availability of whole slide imaging (WSI) data sets (digital slides) from glass slides offers new opportunities for the development of computer-aided diagnostic (CAD) algorithms. With the all-digital pathology workflow that these data sets will enable in the near future, literally millions of digital slides will be generated and stored. Consequently, the field in general and pathologists, specifically, will need tools to help extract actionable information from this new and vast collective repository. Methods: To address this limitation, we designed and implemented a tool (dCORE) to enable the systematic capture of image tiles with constrained size and resolution that contain desired histopathologic features. Results: In this communication, we describe a user-friendly tool that will enable pathologists to mine digital slides archives to create image microarrays (IMAs). IMAs are to digital slides as tissue microarrays (TMAs) are to cell blocks. Thus, a single digital slide could be transformed into an array of hundreds to thousands of high quality digital images, with each containing key diagnostic morphologies and appropriate controls. Current manual digital image cut-and-paste methods that allow for the creation of a grid of images (such as an IMA) of matching resolutions are tedious. Conclusion: The ability to create IMAs representing hundreds to thousands of vetted morphologic features has numerous applications in education, proficiency testing, consensus case review, and research. Lastly, in a manner analogous to the way conventional TMA technology has significantly accelerated in situ studies of tissue specimens use of IMAs has similar potential to significantly accelerate CAD algorithm development.