title: Bayesian atlas estimation for the variability analysis of shape complexes.
abstract: In this paper we propose a Bayesian framework for multiobject atlas estimation based on the metric of currents which permits to deal with both curves and surfaces without relying on point correspondence. This approach aims to study brain morphometry as a whole and not as a set of different components, focusing mainly on the shape and relative position of different anatomical structures which is fundamental in neuro-anatomical studies. We propose a generic algorithm to estimate templates of sets of curves (fiber bundles) and closed surfaces (sub-cortical structures) which have the same "form" (topology) of the shapes present in the population. This atlas construction method is based on a Bayesian framework which brings to two main improvements with respect to previous shape based methods. First, it allows to estimate from the data set a parameter specific to each object which was previously fixed by the user: the trade-off between data-term and regularity of deformations. In a multi-object analysis these parameters balance the contributions of the different objects and the need for an automatic estimation is even more crucial. Second, the covariance matrix of the deformation parameters is estimated during the atlas construction in a way which is less sensitive to the outliers of the population.
authors: CNRS UMR 7225, Inserm UMR-S975, UPMC, CRICM, Paris, France.
authors: Pietro
authors: P
authors: Gori
authors: Aramis Project-Team, Inria Paris-Rocquencourt, Paris, France.
authors: Olivier
authors: O
authors: Colliot
authors: CNRS UMR 7225, Inserm UMR-S975, UPMC, CRICM, Paris, France.
authors: Yulia
authors: Y
authors: Worbe
authors: Aramis Project-Team, Inria Paris-Rocquencourt, Paris, France.
authors: Linda
authors: L
authors: Marrakchi-Kacem
authors: Aramis Project-Team, Inria Paris-Rocquencourt, Paris, France.
authors: Sophie
authors: S
authors: Lecomte
authors: Neurospin, CEA, Gif-Sur-Yvette, France.
authors: Cyril
authors: C
authors: Poupon
authors: CNRS UMR 7225, Inserm UMR-S975, UPMC, CRICM, Paris, France.
authors: Andreas
authors: A
authors: Hartmann
authors: Asclepios Project-Team, Inria Sophia Antipolis, Sophia Antipolis, France.
authors: Nicholas
authors: N
authors: Ayache
authors: Aramis Project-Team, Inria Paris-Rocquencourt, Paris, France.
authors: Stanley
authors: S
authors: Durrleman
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008953:Models, Anatomic; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique