Gilles B Moccozet L Magnenat-Thalmann N 2006 This paper presents a novel approach for multi-organ (musculoskeletal system) automatic registration and segmentation from clinical MRI datasets, based on discrete deformable models (simplex meshes). We reduce the computational complexity using multi-resolution forces, multi-resolution hierarchical collision handling and large simulation time steps (implicit integration scheme), allowing real-time user control and cost-efficient segmentation. Radial forces and topological constraints (attachments) are applied to regularize the segmentation process. Based on a medial axis constrained approximation, we efficiently characterize shapes and deformations. We validate our methods for the hip joint and the thigh (20 muscles, 4 bones) on 4 datasets: average error = 1.5 mm, computation time = 15 min. application/pdf https://sonar.ch/global/documents/21144 eng info:eu-repo/semantics/altIdentifier/doi/10.1007/11866565_36 info:eu-repo/semantics/altIdentifier/pmid/17354902 info:eu-repo/semantics/openAccess Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. - 2006 Algorithms Computer Simulation Humans Image Enhancement Image Interpretation, Computer-Assisted Imaging, Three-Dimensional Magnetic Resonance Imaging Models, Anatomic Models, Biological Musculoskeletal System Pattern Recognition, Automated Reproducibility of Results Sensitivity and Specificity Anatomical modelling of the musculoskeletal system from MRI. http://purl.org/coar/resource_type/c_6501