3D Registration of Articulated Spine Models Using Markov Random FieldsReportar como inadecuado




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1 GALEN - Organ Modeling through Extraction, Representation and Understanding of Medical Image Content Inria Saclay - Ile de France, Ecole Centrale Paris

Abstract : 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 through statistical embedding and image segmentation. 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 MRF 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.

Keywords : Registration Physical modeling Image segmentation Articulated 3D spine model Markov Random Field





Autor: Samuel Kadoury - Nikolaos Paragios -

Fuente: https://hal.archives-ouvertes.fr/



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