A combined active shape and mean appearance model for the reconstruction of segmental bone loss.

Abstract

This study investigates the novel combination of an active shape and mean appearance model to estimate missing bone geometry and density distribution from sparse inputs simulating segmental bone loss of the femoral diaphysis. An active shape Gaussian Process Morphable model was trained on healthy right femurs of South African males to model shape. The density distribution was approximated based on the mean appearance of computed tomography images from the training set. Estimations of diaphyseal resections were obtained by probabilistic fitting of the active shape model to sparse inputs consisting of proximal and distal femoral data on computed tomography images. The resulting shape estimates of the diaphyseal resections were then used to map the mean appearance model to the patients' missing bone geometry, constructing density estimations. In this way, resected bone surfaces were estimated with an average error of 2.24 (0.5)mm. Density distributions were approximated within 87 (0.7)% of the intensity of the original target images before the simulated segmental bone loss. These results fall within the acceptable tolerances required for surgical planning and reconstruction of long bone defects.