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With ever increasing advancements in imaging, there is an increasing abundance of images being acquired in the clinical environment. However, this increase in information can be a burden as well as a blessing as it may require significant amounts of time to interpret the information contained in these images. Computer assisted evaluation is one way in which better use could be made of these images. This thesis presents the combination of texture analysis of images acquired during the treatment of cancer with machine learning in order to improve radiotherapy. The first application is to the prediction of radiation induced pneumonitis. In 13- 37% of cases, lung cancer patients treated with radiotherapy develop radiation induced lung disease, such as radiation induced pneumonitis. Three dimensional texture analysis, combined with patient-specific clinical parameters, were used to compute unique features. On radiotherapy planning CT data of 57 patients, (14 symptomatic, 43 asymptomatic), a Support Vector Machine (SVM) obtained an area under the receiver operator curve (AUROC) of 0.873 with sensitivity, specificity and accuracy of 92%, 72% and 87% respectively. Furthermore, it was demonstrated that a Decision Tree classifier was capable of a similar level of performance using sub-regions of the lung volume. The second application is related to prostate cancer identification. T2 MRI scans are used in the diagnosis of prostate cancer and in the identification of the primary cancer within the prostate gland. The manual identification of the cancer relies on the assessment of multiple scans and the integration of clinical information by a clinician. This requires considerable experience and time. As MRI becomes more integrated within the radiotherapy work flow and as adaptive radiotherapy (where the treatment plan is modified based on multi-modality image information acquired during or between RT fractions) develops it is timely to develop automatic segmentation techniques for reliably identifying cancerous regions. In this work a number of texture features were coupled with a supervised learning model for the automatic segmentation of the main cancerous focus in the prostate - the focal lesion. A mean AUROC of 0.713 was demonstrated with 10-fold stratified cross validation strategy on an aggregate data set. On a leave one case out basis a mean AUROC of 0.60 was achieved which resulted in a mean DICE coefficient of 0.710. These results showed that is was possible to delineate the focal lesion in the majority (11) of the 14 cases used in the study.

Published Article / In this article we will describe the use of 3D medical image information of individual patients as well as selected patient populations, combined with CAE tools and processes, in the rapid product development of custom and standard implantable devices. The combination of medical image information with CAE methods such as CAD, RP, FEA and CFD, allows the engineer to develop implantable devices faster and better, with optimized designs tailored to the anthropometry of the targeted patient (population), using virtual instead of mechanical prototype testing. Case studies will be demonstrated for a variety of surgical fields such as orthopaedic, cranio-maxillofacial and cardiovascular surgery.

Zielstellung: Die Verwendung von Computertomographie und Magnetresonanzto-mographie zur Diagnostik von pathologischen Weichteilveränderungen, wie Läsionen der Speicheldrüsen, wird empfohlen. Diese begründet sich in Ihrer hohen Bildauflö-sung und dem guten Bildkontrast. Nach unserem Wissen gibt es aktuell keine publi-zierte Literatur, welche sich mit der CT- oder MRT-Anatomie der Speicheldrüsen der Katze beschäftigt. Ziel der vorliegenden Arbeit war es daher, Schnittbildmerkmale der Speicheldrüsen zu definieren und Landmarken für eine sichere Identifikation zu be-schreiben. Material und Methoden: Es wurden zwei Gruppen von Katzen untersucht. Die pros-pektiv untersuchten Tiere bestanden aus 16 Katzen, welche aus Gründen euthana-siert wurden, die nicht in Zusammenhang mit der Studie standen. Diese erfuhren ein CT (1 mm Schichtdicke) und 0,5 Tesla MRT (T2W, T2W SPIR, PDW, T1W) des Kop-fes. Im retrospektiven Anteil der Studie wurden CT-Untersuchungen (0,8-1 mm Schichtdicke) von 25 Katzen und MRT-Untersuchungen (T2W, T1W vor und nach Kontrastmittelgabe) von 21 Katzen verwendet. Diese Untersuchungen wurden zwi-schen 2005 und 2013 in unserer Klinik angefertigt. Dabei konnten keine Pathologien in Zusammenhang mit den Speicheldrüsen festgestellt werden. Alle Speicheldrüsen wurden bezüglich Identifizierbarkeit, Abgrenzbarkeit, Größe und Signalverhalten un-tersucht. Wenn vorhanden, wurden anatomische Landmarken die Speicheldrüsen umgebend notiert. Zusammenfassung 80 Ergebnisse: 94,3% der Glandulae parotidae, 90,7% der Glandulae mandibulares und 96,8% der Glandulae zygomaticae konnten mit dem CT identifiziert werden. Beim MRT waren es 94,1% der Glandulae parotidae, 99,2 % der Glandulae mandibulares, 88,9% der Glandulae zygomaticae und 51,4 % der Glandulae buccalis ventralis. In beiden Gruppen konnten keine der restlichen kleinen Speicheldrüsen identifiziert werden, dies galt für beide Schnittbildverfahren. Als hilfreiche anatomi-sche Landmarken konnten der äußere Gehörgang, der M. masseter, der M. pterygo-ideus medialis und der Bulbus oculi herangezogen werden. Die medianen Dichtewer-te lagen für die Glandula parotis, die Glandula mandibularis und die Glandula zygo-matica bei 65 HU, 62 HU und 57 HU. Die Parotis war überwiegend dezent hyperin-tens zur Muskulatur und hypointens zum Fett. Die Mandibularis, Zygomatica, und Glandula buccalis ventralis stellten sich hauptsächlich hyperintens zur Muskulatur und hypointens zu Fettgewebe dar. Dieses Signalverhalten war für T1W, T2W und T2W SPIR (fettunterdrückt) gewichtete Sequenzen gleich. Die Größe war für beide Gruppen und Modalitäten vergleichbar und lag bei circa 17 x 6 mm (gemessen von lateral nach medial und von dorsal nach ventral) bei der Glandula parotis, 8 x 17 mm bei der Glandula mandibularis, 10 x 7 mm bei der Glandula zygomatica und bei der Glandula buccalis ventralis 3 x 10 mm. Schlussfolgerungen: Landmarken und Aussagen über die Signalintensität bezie-hungsweise Dichte und die Größe der Glandula parotis, Glandula mandibularis, Glandula zygomatica und der Glandula buccalis ventralis konnten erhoben werden. Die mangelhafte Identifizierbarkeit der restlichen kleinen Speicheldrüsen, im Ver-gleich zu den oben genannten, kann vor allem mit dem unzureichenden Weichteil-kontrast zum umliegenden Gewebe im CT und der niedrigen Auflösung des Nieder-feld-MRTs erklärt werden. Neuere MRT-Techniken können detailliertere Bilder er-zeugen und so eine Beschreibung der kleinen Speicheldrüsen ermöglichen. / Objective: Due to their high resolution and useful contrast resolution, CT and MRI are recommended methods for diagnosing pathological changes of soft tissues such as lesions of the salivary glands. To our knowledge, no studies about the CT or MR anatomy of the feline salivary glands have been published yet. The aim of the study was to define anatomical characteristics of the salivary glands in cross-sectional im-ages and to describe landmarks for a reliable identification. Materials and methods: Two groups of cats where examined. In a prospective trial, 16 cats, euthanized for reasons unrelated to the study, underwent a head CT (1 mm slice thickness) and 0.5 Tesla MRI scans (T2W, T2W SPIR, PDW, T1W). In a retro-spective trial, the CT scans (0.8-1 mm slice thickness) of 25 cats and the MR scans (T2W, T1W, pre- and post-contrast) of 21 cats were used. These scans were per-formed at our clinic between 2005 and 2013. No signs of salivary-gland-related pa-thologies were visible. All images were reviewed with respect to the following criteria: the presence, delineation and size of the salivary glands as well as the signal intensi-ty. Anatomical landmarks surrounding the glands were also recorded. Results: 94.3% of the parotid glands, 90.7% of the mandibular glands and 96.8% of the zygomatic glands could be delineated using CT. With MRI, 94.1% of the parotid Summary 82 glands, 99.2 % of the mandibular glands, 88.9% of the zygomatic glands and 51,4 % of the ventral buccal glands could be identified. In both groups the other small salivary glands could not be visualized neither in CT nor in MRI. Anatomical landmarks such as the external ear canal, the M. masseter, the M. pterygoideus medialis and the Bulbus oculi facilitate the identification. The mean density of the parotid gland, the mandibular gland and the zygomatic gland was 65 HU, 62 HU and 57 HU, respectively. The parotid gland was mainly modestly hyperintense to muscle and hypointense to fat. The mandibular, zygomatic and ven-tral buccal glands were hyperintense to muscle and hypointense to fat. Signal intensi-ties were similar for T1W, T2W and T2W SPIR images. Sizes were comparable for both groups and modalities and measured about 17x6 mm (lateral to medial and dor-sal to ventral) for the parotid gland, 8 x 17 mm for the mandibular gland, 10x7 mm for the zygomatic gland and 3x10 mm for the ventral buccal gland. Conclusions: Landmarks were recorded and data regarding the density/signal in-tensity and the size of the parotid gland, the mandibular gland, the zygomatic gland and the ventral buccal gland could be gathered. The discrepancy between depicting the small glands and the above mentioned glands can be explained mainly by the lack of contrast to the surrounding tissue in CT and the low spatial resolution in low-field MRI. Advanced MRI techniques are expected to produce more detailed images and therefore those techniques could be used to also describe the small salivary glands.