Anatomical Modelling of the Musculoskeletal System from MRI

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.

[1]  Ronald Fedkiw,et al.  Creating and simulating skeletal muscle from the visible human data set , 2005, IEEE Transactions on Visualization and Computer Graphics.

[2]  Daniel Thalmann,et al.  Interactive modeling of the human musculature , 2001, Proceedings Computer Animation 2001. Fourteenth Conference on Computer Animation (Cat. No.01TH8596).

[3]  Paul A. Yushkevich,et al.  Deformable M-Reps for 3D Medical Image Segmentation , 2003, International Journal of Computer Vision.

[4]  Demetri Terzopoulos,et al.  Symmetry-seeking models and 3D object reconstruction , 1988, International Journal of Computer Vision.

[5]  S. Delp,et al.  Three-Dimensional Representation of Complex Muscle Architectures and Geometries , 2005, Annals of Biomedical Engineering.

[6]  Hervé Delingette,et al.  General Object Reconstruction Based on Simplex Meshes , 1999, International Journal of Computer Vision.

[7]  Johan Montagnat,et al.  4D deformable models with temporal constraints: application to 4D cardiac image segmentation , 2005, Medical Image Anal..

[8]  Nadia Magnenat-Thalmann,et al.  Bone motion analysis from dynamic MRI: acquisition and tracking. , 2005 .

[9]  Andrew P. Witkin,et al.  Large steps in cloth simulation , 1998, SIGGRAPH.

[10]  B L Kaptein,et al.  Estimating muscle attachment contours by transforming geometrical bone models. , 2004, Journal of biomechanics.

[11]  Eugene Fiume,et al.  Anatomically-based models for physical and geometric reconstruction of humans and other animals , 2001 .

[12]  Nadia Magnenat-Thalmann,et al.  Implementing fast cloth simulation with collision response , 2000, Proceedings Computer Graphics International 2000.

[13]  Gabriel Zachmann,et al.  Collision Detection for Deformable Objects , 2004, Comput. Graph. Forum.

[14]  R. Brubaker Models for the perception of speech and visual form: Weiant Wathen-Dunn, ed.: Cambridge, Mass., The M.I.T. Press, I–X, 470 pages , 1968 .

[15]  Pierre Hellier,et al.  Level Set Methods in an EM Framework for Shape Classification and Estimation , 2004, International Conference on Medical Image Computing and Computer-Assisted Intervention.

[16]  Daniel Thalmann,et al.  Real-Time Animation of Realistic Virtual Humans , 1998, IEEE Computer Graphics and Applications.