Encoding probabilistic brain atlases using Bayesian inference.

ABSTRACT: This paper addresses the problem of creating probabilistic brain atlases from manually labeled training data. Probabilistic atlases are typically constructed by counting the relative frequency of occurrence of labels in corresponding locations across the training images. However, such an "averaging" approach generalizes poorly to unseen cases when the number of training images is limited, and provides no principled way of aligning the training datasets using deformable registration. In this paper, we generalize the generative image model implicitly underlying standard "average" atlases, using mesh-based representations endowed with an explicit deformation model. Bayesian inference is used to infer the optimal model parameters from the training data, leading to a simultaneous group-wise reg

SUBMITTER: Van Leemput K 

PROVIDER: S-EPMC3274721 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature