Image data acquisition and segmentation for accurate modeling of the calvarium

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.