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Image Analysis in Surgical Pathology.

Mark C Lloyd1, James P Monaco2, Marilyn M Bui3

  • 1Analytic Microscopy Core, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA; Department of Biological Sciences, University of Chicago Illinois, 845 West Taylor Street, Chicago, IL 60607, USA.

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Summary
This summary is machine-generated.

Digital pathology enables advanced image analysis for surgical samples, offering accurate data collection and new research possibilities. This review covers the history, current technology, and applications of pathology image analysis.

Keywords:
Digital pathologyImage analysisSurgical pathology

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Area of Science:

  • Digital pathology
  • Computational pathology
  • Medical image analysis

Background:

  • Digitization of surgical pathology slides unlocks capabilities beyond traditional microscopy.
  • Digital pathology enables advanced image analysis, offering precise data quantification.
  • Image analysis in pathology provides accurate and reliable data collection.

Purpose of the Study:

  • To review the historical development of image analysis in pathology.
  • To describe the current state of digital pathology technology and processes.
  • To provide examples of current research and clinical applications of image analysis in pathology.

Main Methods:

  • Literature review of historical and current research in pathology image analysis.
  • Analysis of technological advancements in digital pathology.
  • Compilation of case studies and examples of research and clinical use.

Main Results:

  • Digitization of pathology slides is a foundational step for advanced image analysis.
  • Image analysis allows for quantitative data extraction with high accuracy and reliability.
  • The field has evolved from basic image processing to complex analytical tools.

Conclusions:

  • Digital pathology and image analysis are transforming surgical pathology.
  • Quantitative image analysis offers significant potential for both research and clinical practice.
  • Continued development promises further integration of computational tools in pathology workflows.