Durrleman_Media08

Durrleman_Media08 - Inferring brain variability from di...

Info iconThis preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Inferring brain variability from di eomorphic deformations of currents: an integrative approach Stanley Durrleman a , b Xavier Pennec a Alain Trouv b Paul Thompson c Nicholas Ayache a a Asclepios team project, INRIA Sophia Antipolis Mditerrane, 2004 route des Lucioles, 06902 Sophia Antipolis cedex, France b Centre de Mathmatique et Leurs Applications, ENS Cachan, 61 avenue du prsident Wilson, 94235 Cachan cedex, France c Laboratory of NeuroImaging, Dept of Neurology, UCLA School of Medicine, 225E Neuroscience Research Building, Los Angeles, CA, USA Abstract In the context of computational anatomy, one aims at understanding and modelling the anatomy of the brain and its variations across a population. This geometrical variability is often measured from precisely de ned anatomical landmarks such as sulcal lines or meshes of brain structures. This requires (1) to compare geometrical objects without introducing too many non realistic priors and (2) to retrieve the variability of the whole brain from the variability of the landmarks. We propose, in this paper, to infer a statistical brain model from the consistent integration of variability of sulcal lines. The similarity between two sets of lines is measured by a distance on currents that does not assume any type of point correspondences and it is not sensitive to the sampling of lines. This shape similarity measure is used in a di eomorphic registrations which retrieves a single deformation of the whole 3D space. This di eomorphism integrates the variability of all lines in a as spatially consistent manner as possible. Based on repeated pairwise registrations on a large database, we learn how the mean anatomy varies in a population by computing statistics on di eomorphisms. Whereas usual methods lead to descriptive measures of variability, such as variability maps or statistical tests, our model is generative: we can simulate new observations according to the learned probability law on deformations. In practice, this variability captured by the model is synthesized in the principal modes of deformations. As a deformation is dense, we can also apply it to other anatomical structures de ned in the template space. This is illustrated the action of the principal modes of deformations to a mean cortical surface. Eventually, our current-based di eomorphic registration (CDR) approach is carefully compared to a pointwise line correspondences (PLC) method. Variability measures are computed with both methods on the same dataset of sulcal lines. The results suggest that we retrieve more variability with CDR than with PLC, especially in the direction of the lines. Other di erences also appear which highlight the di erent methodological assumptions each method is based on....
View Full Document

Page1 / 18

Durrleman_Media08 - Inferring brain variability from di...

This preview shows document pages 1 - 2. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online