A discriminative feature selection approach for shape analysis: Application to fetal brain cortical folding.

The development of post-processing reconstruction techniques has opened new possibilities for the study of in-utero fetal brain MRI data. Recent cortical surface analysis have led to the computation of quantitative maps characterizing brain folding of the developing brain. In this paper, we describe a novel feature selection-based approach that is used to extract the most discriminative and sparse set of features of a given dataset. The proposed method is used to sparsely characterize cortical folding patterns of an in-utero fetal MR dataset, labeled with heterogeneous gestational age ranging from 26 weeks to 34 weeks. The proposed algorithm is validated on a synthetic dataset with both linear and non-linear dynamics, supporting its ability to capture deformation patterns across the dataset within only a few features. Results on the fetal brain dataset show that the temporal process of cortical folding related to brain maturation can be characterized by a very small set of points, located in anatomical regions changing across time. Quantitative measurements of growth against time are extracted from the set selected features to compare multiple brain regions (e.g. lobes and hemispheres) during the considered period of gestation.