Abstract
Digital pathology, driven by the increasing capabilities of modern computers, is an emerging field within medical research and diagnostics. A re-occurring task in pathology is the analysis of immunohistochemical (IHC) stains, i.e. stains in which a specific type of immune cell is highlighted using corresponding antibodies. Automatic quantification of these images is a challenge due to large image sizes of up to 10 gigapixels, but provides a more objective and reproducible evaluation than the exhaustive task of manual analysis. In this context, we compare counting measures against area-based measures in the case of cytoplasmic and membrane-bound IHC stains. Our evaluation indicates a superior performance of the area-based method which reaches a Jaccard index of approximately 80%, while cell nuclei count-based approaches can be severely affected by variance due to masking effects when the cytoplasmic chromogenic staining covers the blue nuclear counterstain
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© 2017 Springer-Verlag GmbH Deutschland
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Bug, D., Grote, A., Schüler, J., Feuerhake, F., Merhof, D. (2017). Analyzing Immunohistochemically Stained Whole-Slide Images of Ovarian Carcinoma. In: Maier-Hein, geb. Fritzsche, K., Deserno, geb. Lehmann, T., Handels, H., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2017. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54345-0_41
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DOI: https://doi.org/10.1007/978-3-662-54345-0_41
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Publisher Name: Springer Vieweg, Berlin, Heidelberg
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Online ISBN: 978-3-662-54345-0
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