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In this paper, we introduce a novel and efficient approach for inferring articulated 3D spine models from operative images. The problem is formulated as a Markov Random Field which has the ability to encode global structural dependencies to align CT volume images. A personalized geometrical model is first reconstructed from preoperative images before surgery, and subsequently decomposed as a series...
In this paper, we introduce a novel approach based on higher order energy functions which have the ability to encode global structural dependencies to infer articulated 3D spine models to CT volume data. A personalized geometrical model is reconstructed from biplanar X-rays before spinal surgery in order to create a spinal column representation which is modeled by a series of intervertebral transformations...
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