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Introduction to digital image analysis in whole-slide imaging: A white paper from the digital pathology association
Famke Aeffner1, Mark D Zarella2, Nathan Buchbinder3, Marilyn M Bui4, Matthew R Goodman5, Douglas J Hartman6, Giovanni M Lujan7, Mariam A Molani8, Anil V Parwani9, Kate Lillard10, Oliver C Turner11, Venkata N P Vemuri5, Ana G Yuil-Valdes8, Douglas Bowman10
1 Amgen Inc., Amgen Research, Comparative Biology and Safety Sciences, South San Francisco, CA, USA
2 Department of Pathology and Laboratory Medicine, Drexel University, College of Medicine, Philadelphia, PA, USA
3 Proscia, Philadelphia, PA, USA
4 Department of Pathology, Moffitt Cancer Center, Tampa, FL, USA
5 3scan, San Francisco, CA, USA
6 University of Pittsburg Medical Center, Pittsburgh, PA, USA
7 Inform Diagnostics, Irving, TX, USA
8 Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, USA
9 The Ohio State University Medical Center, Columbus, OH, USA
10 Indica Labs, Inc., Corrales, NM, USA
11 Novartis, Novartis Institutes for BioMedical Research, Preclinical Safety, East Hannover, NJ, USA
Correspondence Address:
Dr. Famke Aeffner
Amgen Inc., 1120 Veterans Blvd, South San Francisco, CA 94080
USA
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/jpi.jpi_82_18
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The advent of whole-slide imaging in digital pathology has brought about the advancement of computer-aided examination of tissue via digital image analysis. Digitized slides can now be easily annotated and analyzed via a variety of algorithms. This study reviews the fundamentals of tissue image analysis and aims to provide pathologists with basic information regarding the features, applications, and general workflow of these new tools. The review gives an overview of the basic categories of software solutions available, potential analysis strategies, technical considerations, and general algorithm readouts. Advantages and limitations of tissue image analysis are discussed, and emerging concepts, such as artificial intelligence and machine learning, are introduced. Finally, examples of how digital image analysis tools are currently being used in diagnostic laboratories, translational research, and drug development are discussed.
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