Graph-Based Pancreatic Islet Segmentation for Early Type 2 Diabetes Mellitus on Histopathological Tissue

  • Xenofon Floros
  • Thomas J. Fuchs
  • Markus P. Rechsteiner
  • Giatgen Spinas
  • Holger Moch
  • Joachim M. Buhmann
Conference paper

DOI: 10.1007/978-3-642-04271-3_77

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5762)
Cite this paper as:
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 GZ., Hawkes D., Rueckert D., Noble A., Taylor C. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. MICCAI 2009. Lecture Notes in Computer Science, vol 5762. Springer, Berlin, Heidelberg

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.

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Supplementary material

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Xenofon Floros
    • 1
    • 4
    • 5
  • Thomas J. Fuchs
    • 1
    • 4
    • 5
  • Markus P. Rechsteiner
    • 2
    • 5
  • Giatgen Spinas
    • 3
    • 5
  • Holger Moch
    • 2
    • 5
  • Joachim M. Buhmann
    • 1
    • 5
  1. 1.Department of Computer ScienceETH ZurichSwitzerland
  2. 2.Institute of Pathology, University Hospital ZurichUniversity Zurich 
  3. 3.Division of Endocrinology and DiabetesUniversity Hospital Zurich 
  4. 4.Life Science Zurich PhD Program on Systems Biology of Complex Diseases 
  5. 5.Competence Centre for Systems Physiology and Metabolic DiseasesZurich

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