Weakly supervised cell nuclei detection and segmentation on tissue microarrays of renal clear cell carcinoma

Thomas J. Fuchs, Tilman Lange, Peter J. Wild, Holger Moch, Joachim M. Buhmann

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

15 Scopus citations

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.

Original languageEnglish
Title of host publicationPattern Recognition - 30th DAGM Symposium, Proceedings
Pages173-182
Number of pages10
DOIs
StatePublished - 2008
Externally publishedYes
Event30th DAGM Symposium on Pattern Recognition - Munich, Germany
Duration: 10 Jun 200813 Jun 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5096 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference30th DAGM Symposium on Pattern Recognition
Country/TerritoryGermany
CityMunich
Period10/06/0813/06/08

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