Image microarrays (IMA): Digital pathology’s missing tool
Year: 2011
Type: article
Abstract: 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.
Source: Journal of Pathology Informatics
Authors: 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
Institutions: University of Michigan, University of Pittsburgh, Shadyside Hospital, University of Pittsburgh Medical Center, National Institutes of Health, National Cancer Institute, Massachusetts General Hospital, Rutgers, The State University of New Jersey, Hospital of the University of Pennsylvania, University of Pennsylvania, Weill Cornell Medicine
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,070
OpenAlex ID: W2122596038