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The document consists of a diploma thesis, which describes a completely automated segmentation chain for the bones of the human hip joint from diagnostic MR images including the model-building process for the corresponding anatomical structures. Mainly relying on the well-established model-based segmentation framework, the approach discusses strategies such as the Hough Transform for pre-positioning the involved surface models in the image to enhance robustness of the model-based framework. Furthermore, simple strategies for optimal choice of parameters for the model-based framework are investigated. Theproposed methods have been tested on a set of nine MR images of female patients, all suffering from hip dysplasia.

Electrophysiological simulations of the atria could improve diagnosis and treatment of cardiac arrhythmia, like atrial fibrillation or flutter. For this purpose, a precise segmentation of both atria is needed. However, the atrial epicardium and the electrophysiological structures needed for electrophysiological simulations are barely or not at all detectable in CT-images. Therefore, a model based segmentation of only the atrial endocardium was developed as a landmark generator to facilitate the registration of a finite wall thickness model of the right and left atrial myocardium. It further incorporates atlas information about tissue structures relevant for simulation purposes like Bachmann’s bundle, terminal crest, sinus node and the pectinate muscles. The correct model based segmentation of the atrial endocardium was achieved with a mean vertex to surface error of 0.53 mm for the left and 0.18 mm for the right atrium respectively. The atlas based myocardium segmentation yields physiologically correct results well suited for electrophysiological simulations.

The delineation of anatomical structures in medical images can be achieved in an efficient and robust manner using statistical anatomical organ models, which has been demonstrated for an already considerable set of organs, including the heart. While it is possible to provide models with sufficient shape variability to cope, to a large extent, with inter-patient variability, as long as object topology is conserved, it is a fundamental problem to cope with topological organvariability. We address this by creating a set of model variants and selecting the most appropriate model variant for the patient at hand. We propose a hybrid method combining model-based image analysiswith a guided region growing approach for automated anatomical variant selection and apply it to the left atrium in cardiac CT images. Concerning the human heart, the left atrium is the most variable sub-structure with a variable number of pulmonary veins drainng into it.It is of large clinical interest in the context of atrial fibrillation and related interventions.

For a number of cardiac procedures like the treatment of ventriculartachycardia (VT), coronary artery disease (CAD) and heart failure (HF) the heart anatomy as well as vitality information about the myocardium tissue are required. Thereby the combination of all available information in one patient-adapted 3D heart model is beneficial.Recently, a workflow covering the steps from data acquisition to a patient-adapted integrated model for interventional guidance has been proposed. However, two problems arise in the proposed workflow: The first problem is that the left ventricular epicardium is often notvery well visible on the poor contrast 3D MR images and the secondone is that the anatomy and the vitality information are obtained from two different 3D images acquired with two distinct MR protocols.To solve these problems, a shape-bias for the epicardium is introduced in the model-based segmentation approach taking the patient-specific myocardial thickness into account. Further, a multi-protocol segmentation approach is introduced that allows the information collection into one single heart model by a model-to-image-based registration inherent in the segmentation process.