Statistical Computing on Manifolds: From Riemannian Geometry to Computational Anatomy

Computational anatomy is an emerging discipline that aims at analyzing and modeling the individual anatomy of organs and their biological variability across a population. The goal is not only to model the normal variations among a population, but also discover morphological differences between normal and pathological populations, and possibly to detect, model and classify the pathologies from structural abnormalities. Applications are very important both in neuroscience, to minimize the influence of the anatomical variability in functional group analysis, and in medical imaging, to better drive the adaptation of generic models of the anatomy (atlas) into patient-specific data (personalization). 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 r...

• Type: Conference
• Year: 2008
• Where: ETVC
• Authors: Xavier Pennec
• Keywords: Anatomical Variability, Applied Computing, Computational Anatomy, ETVC 2008, Generic Algorithms