Section: New Results

Diffeomorphic Iterative Centroids for Template Estimation on Large Datasets

Participants : Claire Cury [Correspondant] , Joan Glaunès, Olivier Colliot.

A common approach for analysis of anatomical variability relies on the estimation of a template representative of the population. The Large Deformation Diffeomorphic Metric Mapping is an attractive framework for that purpose. However, template estimation using LDDMM is computationally expensive, which is a limitation for the study of large datasets. We proposed an iterative method which quickly provides a centroid of the population in the shape space. This centroid can be used as a rough template estimate or as initialization of a template estimation method. The approach was evaluated on datasets of real and synthetic hippocampi segmented from brain MRI. The results showed that the centroid is correctly centered within the population and is stable for different orderings of subjects. When used as an initialization, the approach allows to substantially reduce the computation time of template estimation.

More details in [30] .