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.