Weakly Supervised Cell Nuclei Detection and Segmentation on Tissue Microarrays of Renal Clear Cell Carcinoma

Authors:
Thomas J. Fuchs;Tilman Lange;Peter J. Wild;Holger Moch;Joachim M. Buhmann
Affiliations:
Institute for Computational Science, ETH Zürich, Switzerland and Competence Center for Systems Physiology and Metabolic Diseases, ETH Zürich,;Institute for Computational Science, ETH Zürich, Switzerland;Institute of Pathology, University Hospital Zürich, University Zürich, Switzerland;Institute of Pathology, University Hospital Zürich, University Zürich, Switzerland and Competence Center for Systems Physiology and Metabolic Diseases, ETH Zürich,;Institute for Computational Science, ETH Zürich, Switzerland and Competence Center for Systems Physiology and Metabolic Diseases, ETH Zürich,
Venue:
Proceedings of the 30th DAGM symposium on Pattern Recognition
Year:
2008

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Abstract

Renal cell carcinoma (RCC) is one of the ten most frequent malignancies in Western societies and can be diagnosed by histological tissue analysis. Current diagnostic rules rely on exact counts of cancerous cell nuclei which are manually counted by pathologists.We propose a complete imaging pipeline for the automated analysis of tissue microarrays of renal cell cancer. At its core, the analysis system consists of a novel weakly supervised classification method, which is based on an iterative morphological algorithm and a soft-margin support vector machine. The lack of objective ground truth labels to validate the system requires the combination of expert knowledge of pathologists. Human expert annotations of more than 2000 cell nuclei from 9 different RCC patients are used to demonstrate the superior performance of the proposed algorithm over existing cell nuclei detection approaches.