1st Congress of the International Academy of Digital Pathology Quebec City, Canada, August 3–5, 2011. Part IIView this Special Issue
Arvydas Laurinavicius, Aida Laurinaviciene, Darius Dasevicius, Nicolas Elie, Benoît Plancoulaine, Catherine Bor, Paulette Herlin, "Digital Image Analysis in Pathology: Benefits and Obligation", Analytical Cellular Pathology, vol. 35, Article ID 243416, 4 pages, 2012. https://doi.org/10.3233/ACP-2011-0033
Digital Image Analysis in Pathology: Benefits and Obligation
Pathology has recently entered the era of personalized medicine. This brings new expectations for the accuracy and precision of tissue-based diagnosis, in particular, when quantification of histologic features and biomarker expression is required. While for many years traditional pathologic diagnosis has been regarded as ground truth, this concept is no longer sufficient in contemporary tissue-based biomarker research and clinical use. Another major change in pathology is brought by the advancement of virtual microscopy technology enabling digitization of microscopy slides and presenting new opportunities for digital image analysis. Computerized vision provides an immediate benefit of increased capacity (automation) and precision (reproducibility), but not necessarily the accuracy of the analysis. To achieve the benefit of accuracy, pathologists will have to assume an obligation of validation and quality assurance of the image analysis algorithms. Reference values are needed to measure and control the accuracy. Although pathologists' consensus values are commonly used to validate these tools, we argue that the ground truth can be best achieved by stereology methods, estimating the same variable as an algorithm is intended to do. Proper adoption of the new technology will require a new quantitative mentality in pathology. In order to see a complete and sharp picture of a disease, pathologists will need to learn to use both their analogue and digital eyes.
Copyright © 2012 Hindawi Publishing Corporation and the authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.