Surface/Volume-Based Articulated 3D Spine Inference through Markov Random Fields

This paper presents a method towards inferring personalized 3D spine models to intraoperative CT data acquired for corrective spinal surgery. An accurate 3D reconstruction from standard X-rays is obtained before surgery to provide the geometry of vertebrae. The outcome of this procedure is used as basis to derive an articulated spine model that is represented by consecutive sets of intervertebral articulations relative to rotation and translation parameters (6 degrees of freedom). Inference with respect to the model parameters is then performed using an integrated and interconnected Markov Random Field graph that involves singleton and pairwise costs. Singleton potentials measure the support from the data (surface or image-based) with respect to the model parameters, while pairwise constraints encode geometrical dependencies between vertebrae. Optimization of model parameters in a multi-modal context is achieved using efficient linear programming and duality. We show successful image registration results from simulated and real data experiments aimed for image-guidance fusion.