Reconstruction of the Pelvic Region Based on the Statistical Shape Modeling

Abstract

Up until now the reconstruction of the pelvis defects after bone tumor resection has typically been by autologous or allogenous grafts. These approaches were highly unsatisfied because of large shape differences between the harvested transplant and the site to be reconstructed. There exists a huge demand for patient-specific and anatomically shaped implants. In this paper we propose a new pelvis reconstruction planning approach based on the statistical shape modeling. For generation of the statistical pelvis shape model a large data pool of CT datasets has been collected. Afterwards, the 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) [4] applied for estimating the anatomically optimal graft or implant geometry. In this work we demonstrate on a clinical pelvis reconstruction case that the using of statistical shape models in the oncologic surgery planning is a robust and very promising method. A quantitative evaluation of the matching quality and the convergence process is given.