Journal of Pathology Informatics Journal of Pathology Informatics
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REVIEW ARTICLE
Year : 2018  |  Volume : 9  |  Issue : 1  |  Page : 40

Twenty years of digital pathology: An overview of the road travelled, what is on the horizon, and the emergence of vendor-neutral archives


1 Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
2 Department of Biomedical Informatics, Emory University, GA, USA
3 Department of Pathology and Laboratory Medicine, Children's Healthcare of Atlanta, GA, USA
4 Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
5 Department of Computer Science, University of Maryland, College Park, MD, USA

Correspondence Address:
Dr. Liron Pantanowitz
Department of Pathology, University of Pittsburgh Medical Center Shadyside, Cancer Pavilion Suite 201, 5150 Centre Avenue, Pittsburgh, PA 15232
USA
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jpi.jpi_69_18

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Almost 20 years have passed since the commercial introduction of whole-slide imaging (WSI) scanners. During this time, the creation of various WSI devices with the ability to digitize an entire glass slide has transformed the field of pathology. Parallel advances in computational technology and storage have permitted rapid processing of large-scale WSI datasets. This article provides an overview of important past and present efforts related to WSI. An account of how the virtual microscope evolved from the need to visualize and manage satellite data for earth science applications is provided. The article also discusses important milestones beginning from the first WSI scanner designed by Bacus to the Food and Drug Administration approval of the first digital pathology system for primary diagnosis in surgical pathology. As pathology laboratories commit to going fully digitalize, the need has emerged to include WSIs into an enterprise-level vendor-neutral archive (VNA). The different types of VNAs available are reviewed as well as how best to implement them and how pathology can benefit from participating in this effort. Differences between traditional image algorithms that extract pixel-, object-, and semantic-level features versus deep learning methods are highlighted. The need for large-scale data management, analysis, and visualization in computational pathology is also addressed.


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