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Accuracy of the patient-model is a critical point in robot assisted surgery. When performing craniotomies, the dura mater must not be perforated. Hence bone width is of particular interest. The influence of imaging and segmentation on accuracy of the width of the bone-model was investigated. A human cadaver head was scanned with a CT-scanner under a variety of image acquisition parameters. Bone was segmented from these image data sets using threshold based segmentation with different settings for the lower threshold. From these volume data sets surface models of the bone were generated. The real width of the bone of the skull was measured at several positions. Using fiducial marker registration, these measured values were compared to the corresponding positions in the bone-models. CT-scan imaging with a slice thickness and slice distance of 1.5 to 2mm and a segmentation of bone with a lower threshold of 300 or 400 Hounsfield Units resulted in models with an average accuracy of 0.4mm for bone-width. However, at some points these models were too thin by up to 0.9mm. More accurate models are needed. It has to be evaluated, whether CT imaging with higher resolution or more sophisticated segmentation algorithms can reduce the scatter.

When planning craniofacial surgical interventions, the ideal appearance of the patient is very important. The final appearance should be as close as possible to that which the patient would have if he/she were without defects. Our first step towards achieving this is to build a database containing sets of three-dimensional CT images that allows for comparison of the shape of a patient with defects to the typical shape of an age- and sex-matched "average" person without defects. We started to collect CT data from patients without pathologies and, in co-operation with two radiology institutes (in Mannheim and Heidelberg), over 100 CT data sets have now been collected and classified according to age and sex. It is necessary to choose an appropriate statistical method to calculate the norm data from the different data sets. Based on the statistical method, an age- and sex-matched "average" model of the anatomy will be created.

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Three-dimensional models of the patient's anatomy are the basis for modern applications in computer-assisted surgery or radiology. To create these models the data is processed in a series of steps that transforms the initial raw data (generally CT/MRT volume images) into the three-dimensional models (e.g. triangle meshes). In doing so, a multitude of parameters adjustments, interactions and decisions are necessary to be made by the physician in order to create the models adapted to the specific needs of the patient and to the surgery. The quality of the models therefore strongly depends on that process chain. As a result of the high requirements on the quality and security of a clinical application the quality of the models itself and the capability to use them flexibly are essential. This work present a method to adjust and optimise the creation of the models and to asses their quality afterwards.