Header

UZH-Logo

Maintenance Infos

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


Fuchs, T J; Lange, T; Wild, P J; Moch, H; Buhmann, J M (2008). Weakly supervised cell nuclei detection and segmentation on tissue microarrays of renal clear cell carcinoma. In: Rigoll, G. Pattern Recognition. Berlin, DE: Springer, 173-182.

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.

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.

Statistics

Citations

Dimensions.ai Metrics
7 citations in Web of Science®
8 citations in Scopus®
10 citations in Microsoft Academic
Google Scholar™

Altmetrics

Downloads

1 download since deposited on 30 Jan 2009
0 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Institute of Pathology and Molecular Pathology
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:2008
Deposited On:30 Jan 2009 09:50
Last Modified:20 Feb 2018 13:07
Publisher:Springer
Series Name:Lecture Notes in Computer Science
ISBN:978-3-540-69320-8
OA Status:Closed
Publisher DOI:https://doi.org/10.1007/978-3-540-69321-5_18
Related URLs:http://opac.nebis.ch/F/?local_base=NEBIS&con_lng=GER&func=find-b&find_code=SYS&request=005651811

Download