Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-27430
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, 633-640. ISBN 978-3-642-04270-6.
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.
|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
|DDC:||610 Medicine & health|
|Deposited On:||05 Feb 2010 12:55|
|Last Modified:||23 Nov 2012 14:09|
|Series Name:||Lecture Notes in Computer Science|
|ISSN:||0302-9743 (P) 1611-3349 (E)|
|Additional Information:||The original publication is available at www.springerlink.com|
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