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Proceedings Paper

Objective tumour heterogeneity determination in gliomas
Author(s): Dirk Simon; Jan Klein; Jan Rexilius; Bram Stieltjes
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Paper Abstract

Diffusion weighted imaging (DWI) derived apparent diffusion coefficient (ADC) values are known to correlate inversely to tumour cellularity in brain tumours. The average ADC value increases after successful chemotherapy, radiotherapy or a combination of both and can be therewith used as a surrogate marker for treatment response. Moreover, high and low malignant areas can be distinguished. The main purpose of our project was to develop a software platform that enables the automated delineation and ADC quantification of different tumour sections in a fast, objective, user independent manner. Moreover, the software platform allows for an analysis of the probability density of the ADC in high and low malignant areas in ROIs drawn on conventional imaging to create a ground truth. We tested an Expectation Maximization algorithm with a Gaussian mixture model to objectively determine tumour heterogeneity in gliomas because of yielding Gaussian distributions in the different areas. Furthermore, the algorithm was initialized by seed points in the areas of the gross tumour volume and the data indicated that an automatic initialization should be possible. Thus automated clustering of high and low malignant areas and subsequent ADC determination within these areas is possible yielding reproducible ADC measurements within heterogeneous gliomas.

Paper Details

Date Published: 3 March 2009
PDF: 10 pages
Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72601X (3 March 2009); doi: 10.1117/12.812521
Show Author Affiliations
Dirk Simon, German Cancer Research Ctr. (Germany)
Jan Klein, MeVis Research GmbH (Germany)
Jan Rexilius, MeVis Research GmbH (Germany)
Bram Stieltjes, German Cancer Research Ctr. (Germany)

Published in SPIE Proceedings Vol. 7260:
Medical Imaging 2009: Computer-Aided Diagnosis
Nico Karssemeijer; Maryellen L. Giger, Editor(s)

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