Paper Info
Title
Graph-Based Pancreatic Islet Segmentation for Early Type 2 Diabetes Mellitus on Histopathological Tissue
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.
Year
DOI
Venue
2009
10.1007/978-3-642-04271-3_77
MICCAI
Keywords
Field
DocType
diabetes mellitus
Active contour model,Pattern recognition,Computer science,Segmentation,Medical imaging,Local binary patterns,Pancreatic islets,Artificial intelligence,Random forest,Cross-validation,Pathology,Beta cell
Conference
Volume
Issue
ISSN
12
Pt 2
0302-9743
Citations 
PageRank 
References 
0
0.34
6
Authors
6
Name
Order
Citations
PageRank
Xenofon Floros100.68
Thomas J. Fuchs234322.48
Markus P. Rechsteiner300.34
Giatgen Spinas400.34
H Moch513115.90
joachim m buhmann64363730.34