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Automatic quantification of immunohistochemically stained cell nuclei based on standard reference cells
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
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1998 (English)In: Analytical Cellular Pathology, ISSN 0921-8912, E-ISSN 1878-3651, Vol. 17, no 2, 111-23 p.Article in journal (Refereed) Published
Abstract [en]

A fully automatic method for quantification of images of immunohistochemically stained cell nuclei by computing area proportions, is presented. Agarose embedded cultured fibroblasts were fixed, paraffin embedded and sectioned at 4 microm. They were then stained together with 4 microm sections of the test specimen obtained from bladder cancer material. A colour based classifier is automatically computed from the control cells. The method was tested on formalin fixed paraffin embedded tissue section material, stained with monoclonal antibodies against the Ki67 antigen and cyclin A protein. Ki67 staining results in a detailed nuclear texture with pronounced nucleoli and cyclin A staining is obtained in a more homogeneously distributed pattern. However, different staining patterns did not seem to influence labelling index quantification, and the sensitivity to variations in light conditions and choice of areas within the control population was low. Thus, the technique represents a robust and reproducible quantification method. In tests measuring proportions of stained area an average standard deviation of about 1.5% for the same field was achieved when classified with classifiers created from different control samples.

Place, publisher, year, edition, pages
1998. Vol. 17, no 2, 111-23 p.
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Medical and Health Sciences
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URN: urn:nbn:se:uu:diva-63333PubMedID: 10052635OAI: oai:DiVA.org:uu-63333DiVA: diva2:91244
Available from: 2005-09-23 Created: 2005-09-23 Last updated: 2017-11-30Bibliographically approved

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Ranefall, PetterWester, KennethAndersson, Ann-CatrinBusch, ChristerBengtsson, Ewert

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