Morphometry of anatomical shape complexes with dense deformations and sparse parameters

Stanley Durrleman (1) , Marcel Prastawa (2) , Nicolas Charon (3) , Julie R Korenberg (4) , S. Joshi (2) , Guido Gerig (2) , Alain Trouvé (3)

1 ARAMIS - Algorithms, models and methods for images and signals of the human brain = Algorithmes, modèles et méthodes pour les images et les signaux du cerveau humain [ICM Paris]
2 SCI Institute - Scientific Computing and Imaging Institute
3 CMLA - Centre de Mathématiques et de Leurs Applications
4 University of Utah

We propose a generic method for the statistical analysis of collections of anatomical shape complexes, namely sets of surfaces that were previously segmented and labeled in a group of subjects. The method estimates an anatomical model, the template complex, that is representative of the population under study. Its shape reflects anatomical invariants within the dataset. In addition, the method automatically places control points near the most variable parts of the template complex. Vectors attached to these points are parameters of deformations of the ambient 3D space. These deformations warp the template to each subject's complex in a way that preserves the organization of the anatomical structures. Multivariate statistical analysis is applied to these deformation parameters to test for group differences (...)

Domaines: Imagerie médicale

Stanley Durrleman, Marcel Prastawa, Nicolas Charon, Julie R Korenberg, S. Joshi, et al.. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage, 2014, ⟨10.1016/j.neuroimage.2014.06.043⟩. ⟨hal-01015771⟩