Unsupervised Reconstruction of a Patient-Specific Surface Model of a Proximal Femur from Calibrated Fluoroscopic Images

In this paper, we present an unsupervised 2D/3D reconstruction scheme combining a parameterized multiple-component geometrical model and a point distribution model, and show its application to automatically reconstruct a surface model of a proximal femur from a limited number of calibrated fluoroscopic images with no user intervention at all. The parameterized multiple-component geometrical model is regarded as a simplified description capturing the geometrical features of a proximal femur. Its parameters are optimally and automatically estimated from the input images using a particle filter based inference method. The estimated geometrical parameters are then used to initialize a point distribution model based 2D/3D reconstruction scheme for an accurate reconstruction of a surface model of the proximal femur. We designed and conducted in vitro and in vivo experiments to compare the present unsupervised reconstruction scheme to a supervised one. An average mean error of 1.2 mm was found when the supervised reconstruction scheme was used. It increased to 1.3 mm when the unsupervised one was used. However, the unsupervised reconstruction scheme has the advantage of elimination of user intervention, which holds the potential to facilitate the application of the 2D/3D reconstruction in surgical navigation.