Using of statistical shape models for pelvis reconstruction in the oncologic surgery

The reconstruction of the osseous structures in the pelvic region after bone tumour resection is highly complex and challenging. Up to now the reconstruction of the pelvis defects by autologous or allogenous grafts (for instance the fibula transplants) are highly unsatisfied because of large shape differences. Therefore there is a huge demand for patient-specific and anatomically shaped implants. Our pelvis reconstruction planning approach is based on the statistical shape model. For generation of the statistical pelvis shape model a large data pool of CT datasets has been collected. The following CT data segmentation and surface processing methods delivered the required pelvis geometries. Via Procrustes analysis of the collected pelvis surfaces the parameterized pelvis shape mean model has been calculated and the principal component analysis (PCA) [3] applied for estimating the anatomically optimal graft or implant geometry. We will demonstrate on the clinical pelvis reconstruction case that the using of statistical shape models in the oncologic surgery planning is robust and very promising method.