A multiscale algorithm for computing realistic image transformation: application to the modelling of fetal brain growth

Abstract

We propose to establish a continuous trajectory of brain growth across pregnancy using geodesic regression in the Large Deformation Diffeomorphic Metric Mapping framework. One is usually faced with two issues when estimating high dimensional transformations: the elevated risk of trapping the optimization in an unrealistic local minimum and the fact that deformations are constrained to a single scale. To tackle these issues, we introduce a coarse-to-fine optimization strategy based on multiscale parametrizations of objects and deformations. Experiments on fetal brain Magnetic Resonance Images show that the multiscale strategy can generate more natural images of the fetal brain across pregnancy, which offers an interesting perspective for the quantitative analysis of normal and abnormal brain growth.