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Graph-based pancreatic islet segmentation for early type 2 diabetes mellitus on histopathological tissue


Floros, X; Fuchs, T J; Rechsteiner, M P; Spinas, G; Moch, H; Buhmann, J M (2009). Graph-based pancreatic islet segmentation for early type 2 diabetes mellitus on histopathological tissue. In: Yang, G Z; Hawkes, D; Rueckert, D; Noble, A; Taylor, C. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009, 12th International Conference, London, UK, September 20-24, 2009, Proceedings, Part 2. Berlin: Springer, 633-640.

Abstract

It is estimated that in 2010 more than 220 million people will be affected by type 2 diabetes mellitus (T2DM). Early evidence indicates that specific markers for alpha and beta cells in pancreatic islets of Langerhans can be used for early T2DM diagnosis. Currently, the analysis of such histological tissues is manually performed by trained pathologists using a light microscope. To objectify classification results and to reduce the processing time of histological tissues, an automated computational pathology framework for segmentation of pancreatic islets from histopathological fluorescence images is proposed. Due to high variability in the staining intensities for alpha and beta cells, classical medical imaging approaches fail in this scenario.
The main contribution of this paper consists of a novel graph-based segmentation approach based on cell nuclei detection with randomized tree ensembles. The algorithm is trained via a cross validation scheme on a ground truth set of islet images manually segmented by 4 expert pathologists. Test errors obtained from the cross validation procedure demonstrate that the graph-based computational pathology analysis proposed is performing competitively to the expert pathologists while outperforming a baseline morphological approach.

It is estimated that in 2010 more than 220 million people will be affected by type 2 diabetes mellitus (T2DM). Early evidence indicates that specific markers for alpha and beta cells in pancreatic islets of Langerhans can be used for early T2DM diagnosis. Currently, the analysis of such histological tissues is manually performed by trained pathologists using a light microscope. To objectify classification results and to reduce the processing time of histological tissues, an automated computational pathology framework for segmentation of pancreatic islets from histopathological fluorescence images is proposed. Due to high variability in the staining intensities for alpha and beta cells, classical medical imaging approaches fail in this scenario.
The main contribution of this paper consists of a novel graph-based segmentation approach based on cell nuclei detection with randomized tree ensembles. The algorithm is trained via a cross validation scheme on a ground truth set of islet images manually segmented by 4 expert pathologists. Test errors obtained from the cross validation procedure demonstrate that the graph-based computational pathology analysis proposed is performing competitively to the expert pathologists while outperforming a baseline morphological approach.

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Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic for Endocrinology and Diabetology
04 Faculty of Medicine > University Hospital Zurich > Institute of Surgical Pathology
Dewey Decimal Classification:610 Medicine & health
Language:German
Date:2009
Deposited On:05 Feb 2010 11:55
Last Modified:05 Apr 2016 13:45
Publisher:Springer
Series Name:Lecture Notes in Computer Science
Number:5762
ISSN:0302-9743 (P) 1611-3349 (E)
ISBN:978-3-642-04270-6
Additional Information:The original publication is available at www.springerlink.com
Publisher DOI:10.1007/978-3-642-04271-3_77
Related URLs:http://opac.nebis.ch/F/?local_base=NEBIS&con_lng=GER&func=find-b&find_code=SYS&request=005897222
Permanent URL: http://doi.org/10.5167/uzh-27430

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