Multi-subject Variational Registration for Probabilistic Unbiased Atlas Generation

De Craene, Mathieu [UCL] Du Bois d'Aische, Aloys [UCL] Macq, Benoît [UCL] Warfield, Simon

This paper introduces a new metric to gather a large collection of segmented images into a same reference system. Different positions for each subject (pose parameters) as well as high energy shape variations need to be compensated before performing statistical analysis (like principal components analysis) on the database. The atlas is obtained as the hidden variable of an Expectation-Maximization (EM), looking for the right signal intensity at each voxel in the collection of subjects. Each subject is aligned on the current probabilistic atlas by maximizing mutual information. A fast stochastic optimization algorithm is used for estimating pose and scale parameters and a variational approach has been designed to estimate non-rigid transformations. We illustrate the effectiveness of this method for the alignment of 31 brain segmented in 4 labels : background, white and gray matter and ventricles. Our approach has the advantage of keeping a reasonably low complexity even for large databases