Statistical Computing on Non-Linear Spaces for Computational Anatomy

International audienceComputational anatomy is an emerging discipline that aims at analyzing and modeling the individual anatomy of organs and their biological variability across a population. However, understanding and modeling the shape of organs is made difficult by the absence of physical models for comparing different subjects, the complexity of shapes, and the high number of degrees of freedom implied. Moreover, the geometric nature of the anatomical features usually extracted raises the need for statistics on objects like curves, surfaces and deformations that do not belong to standard Euclidean spaces. We explain in this chapter how the Riemannian structure can provide a powerful framework to build generic statistical computing tools. We show that few computational tools derive for each Riemannian metric can be used in practice as the basic atoms to build more complex generic algorithms such as interpolation, filtering and anisotropic diffusion on fields of geometric features. This computational framework is illustrated with the analysis of the shape of the scoliotic spine and the modeling of the brain variability from sulcal lines where the results suggest new anatomical findings

reconstruction de formes pour la chirurgie assistée par ordinateur basée sur l'enregistrement

This thesis addresses the problem of reconstructing 3D anatomical surfaces based on intraoperatively acquired sparse scattered point data and few calibrated X-ray images. The approacli consists in matching the data with a statistical deformable shape rnodel thus incorporating a priori knowledge into the reconstruction process. Computing such a statistical model requires prior shape analysis in a given population. A new method based on a generic model of the object is used to segment training shapes and to establisli point to point correspondence simultaneously in a set of CT images. Scattered point data are then matched witli the statistical model using a non rigid 3D/3D registration algorithm. The application of this method for intra and extrapolation of sparse point data is demonstrated within a system for computer assisted reconstruction of the anterior cruciate ligament. To reconstruct a surface from few calibrated X-ray images the statistical shape model is matched to the object contours segmented on the calibrated X-ray images based on a new non rigid 3D/2D registration method. Experiments are performed on a statistical model of lumbar vertebrae for the clinical application of pedicle screw placement. It is further shown that hybrid matching combining both, 3D/3D and 3D/2D registration, miglit be an interesting option for certain Computer Assisted Surgery Applications. AbstractL'objectif de cette thèse est la reconstruction de surfaces anatomiques à partir d'un nombre restreint de radiographies et de points acquis en phase per-operatoire. L'approche proposée repose sur une mise en correspondance des données avec un modèle déformable statistique afin d'incorporer de la connaissance à priori sur la forme de l'objet à reconstruire. L'élaboration d'un tel modèle statistique nécessite l'analyse de forme dans une population donnée. Pour cette analyse un modèle générique de l'objet est utilisé afin d'effectuer simultanément la segmentation des structures et la mise en correspondance de points appariés dans un ensemble d'examens tomodensitométriques. La reconstruction à partir d'un nuage de points est effectuée par une méthode de recalage 3D/3D non rigide. L'application de cette technique d'interpolation et d'extrapolation de données incomplètes est montrée dans un système pour la reconstruction du ligament croisé antérieur. Pour la reconstruction à partir de radiographies une méthode de recalage 3D/2D non rigide est proposée afin de mettre en correspondance le modèle statistique avec les contours de l'objet segmenté dans les radiographies calibrées. Des expérimentations ont été effectuées avec un modèle statistique de vertèbres lombaires, en vue de l'application clinique du vissage pédiculaire. De plus il est montré que la mise en correspondance hybride combinant le recalage 3D/3D et le recalage 3D/2D pourrait être une option intéressante pour certaines applications dans le domaine des Gestes Médicaux Chirurgicaux Assistés par Ordinateur

Reconstruction de formes pour les GMCAO par recalage non rigide de modèles statistiques avec des radiographies et un nuage de points acquis en phase per-opératoire

This thesis addresses the problem of reconstructing 3D anatomical surfaces based on intra-operatively acquired sparse scattered point data and few calibrated X-ray images. The approach consists in matching the data with a statistical deformable shape model thus incorporating a priori knowledge into the reconstruction process (..) It is further shown that hybrid matching combining both, 3D/3D and 3D/2D registration, might be an interesting option for certain Computer Assisted Surgery Applications.GRENOBLE1-BU Médecine pharm. (385162101) / SudocPARIS-BIUP (751062107) / SudocGRENOBLE-MI2S (384212302) / SudocSudocFranceF

Modélisation et chirurgie assistée par ordinateur

Medical information has to be acquired, shared, enhanced and used to result in a coordinated action for individual care of general healthcare. Modelling is a key component of this transformation process. This is not only true at a macroscopic level but also for many sub-domains of medical information engineering. In this chapter, we intend to make clearer the important role of models for Computer Aided Medical Interventions. After having presented a general overview of the needs in terms of modelling, we will focus on two types of models - statistical and biomechanical ones - and we will give examples of their clinical applications. These examples are work in progress developed in collaboration with the Grenoble and Toulouse (Purpan) university hospitals