Dynamic Tree-Based Large-Deformation Image Registration for Multi-atlas Segmentation.

Multi-atlas segmentation is a powerful approach to automated anatomy delineation via fusing label information from a set of spatially normalized atlases. For simplicity, many existing methods perform pairwise image registration, leading to inaccurate segmentation especially when shape variation is large. In this paper, we propose a dynamic tree-based strategy for effective large-deformation registration and multiatlas segmentation. To deal with local minima caused by large shape variation, coarse estimates of deformations are first obtained via alignment of automatically localized landmark points. A dynamic tree capturing the structural relationships between images is then used to further reduce misalignment errors. Validation on two real human brain datasets, ADNI and LPBA40, shows that our method significantly improves registration and segmentation accuracy.