Simultaneous pose estimation and patient-specific model reconstruction from single image using maximum penalized likelihood estimation (MPLE)

Abstract

Pose estimation and shape reconstruction are two common problems in pattern recognition, which oftentimes are tackled separately. But in some medical applications, both pose and shape of a target anatomy are crucial and have to be estimated from intra-operative two-dimensional images. As pose estimation and shape reconstruction are two coupled problems, previous feature-based methods solved the problems in consecutive stages utilizing statistical shape models (SSMs). Only the mean shape of SSM is used to estimate the pose by finding paired correspondences in the first stage, based on which SSM-regularized surface deformations are performed in the following stages. Such a strategy heavily depends on the paired correspondences. In this paper, bypassing correspondence establishment, a novel method is proposed to simultaneously optimize pose and shape by formulating the coupled problems as a maximum penalized likelihood estimation (MPLE). It models oriented object contours as a mixture of von Mises–Fisher Gaussian distributions, and solves the MPLE effectively using a global optimizer. It utilize the entire knowledge of SSM in both solving pose and reconstructing shape, providing robustness to large offsets in initializations. Leave-one-out cross-validations on 19 dry cadaveric femurs were performed using simulated X-ray images with accurate ground-truth, under various initial conditions. Our method achieved sub-degree rotational and sub-millimeter in-plane translational pose estimation errors, and an approximately one millimeter average mean surface-to-surface distance in shape reconstruction. The reconstruction accuracy is comparable to those reported in the literature using two or more images. The experiment results are encouraging and indicate that an accurate simultaneous 3D–2D pose estimation and surface reconstruction is achievable from one single image.