Segmentation of the facial nerve using active appearance models

A large-scale invasive approach is today's standard technique for temporal bone surgery, although it is time-consuming and causes significant tissue damage. One could overcome these disadvantages with a minimally-invasive operation method, where the whole surgery is performed through three drill canals. In order not to damage vital structures by drilling the canals, a detailed and exact operation planning is crucial, including the segmentation of all critical structures in preoperative computer tomographic data. The facial nerve is one of the important collision structures in the temporal bone region. Its segmentation is a challenging task because of its small size, its weak contrast to adjacent structures and large inter-patient variations. In this thesis, a semi-automatic two-step algorithm for the segmentation of the facial nerve is presented. We propose an Active Appearance Model based method for the extraction of the facial nerve's centerline and evaluate four different texture descriptors in this context. For the subsequent full structure segmentation, we introduce a ray-based approach that uses the centerline for initialization. The approaches for both centerline extraction and full structure segmentation yield reliable results. They show the best segmentation quality using an introduced intensity histogram as texture descriptor.