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International audienceThe 3D modeling of the proximal femur is a valuable diagnostic tool for orthopedic surgery planning. The use of computed tomography is the most prominent modality to visualize bones both in terms of resolution as well as in term of bone/tissue separation. Towards reducing the impact of radiation to the patient, low-dose X-ray imaging systems have been introduced while still providing partial views with rather low signal-to-noise ratio. In this paper, we focus on automating the 3D proximal femur reconstruction from simultaneously acquired 2D views. A deformable model represented by triangulated mesh surfaces extends to a linear sub-space describing the variations across individuals. Segmentation consists of inferring a global deformation of 3D model followed by a local adaption based on the most prominent combination of the sub-space parameters. The ba-sis of which relies on the minimization of a cost function based on the biplanar projection of this model. To this end, we employ an active region model that aims at optimizing the 3D model parameters such that projection of surface is attracted from edge potentials, while creating an optimal partition between the bone class and the remaining structures. The global parameters of the model and the local ones are optimized through a gradient-free approach. Promising results demonstrate the potentials of our method compared to a supervised reconstruction technique

3D Model-based Reconstruction of the Proximal Femur from Low-dose Biplanar X-Ray Images