Image microarrays (IMA): Digital pathology's missing tool
| Autor: | Anant Madabhushi, Stephen M. Hewitt, Sinchita Roy-Chowdhuri, Armando C. Filie, Michael Feldman, James Monaco, Michael R. Emmert-Buck, Victor Brodsky, Ulysses J. Balis, Jeffrey C. Hanson, Jaime Rodriguez-Canales, Jason D. Hipp, Liron Pantanowitz, John E. Tomaszewski, Natalie Nc Shih, Giuseppe Giaccone, Jerome Cheng, Yukako Yagi |
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| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
WSI
SIVQ Computer science Digital pathology Health Informatics CAD lcsh:Computer applications to medicine. Medical informatics Grid computer.software_genre Field (computer science) Computer Science Applications Pathology and Forensic Medicine Image (mathematics) IMA Digital image Workflow lcsh:Pathology Key (cryptography) lcsh:R858-859.7 Original Article Data mining TMA computer lcsh:RB1-214 |
| Zdroj: | Journal of Pathology Informatics Journal of Pathology Informatics, Vol 2, Iss 1, Pp 47-47 (2011) |
| ISSN: | 2153-3539 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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