Introduction to digital image analysis in whole-slide imaging: A white paper from the digital pathology association

Famke Aeffner, Mark D. Zarella, Nathan Buchbinder, Marilyn M. Bui, Matthew R. Goodman, Douglas J. Hartman, Giovanni M. Lujan, Mariam A. Molani, Anil V. Parwani, Kate Lillard, Oliver C. Turner, Venkata N.P. Vemuri, Ana G. Yuil-Valdes, Douglas Bowman

Research output: Contribution to journalArticle

34 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number9
JournalJournal of Pathology Informatics
Volume10
Issue number1
DOIs
StatePublished - Jan 1 2019

Keywords

  • Artificial intelligence
  • computational pathology
  • digital pathology
  • image analysis
  • quantitative image analysis
  • whole-slide imaging

ASJC Scopus subject areas

  • Pathology and Forensic Medicine
  • Health Informatics
  • Computer Science Applications

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