ABSTRACT
Registration is used for several purposes during multi-atlas segmentation. Atlases may be pre-aligned using rigid or deformable registration, and online registration may be performed during atlas selection. Furthermore, all atlas-based methods rely on deformable registration to map atlases onto the target image and the accuracy of the segmentation depends, to a large degree, on the deformable registration strategy undertaken. This chapter examines the influence of deformable registration parameter selection for two popular registration methods, the B-spline and demons algorithms. For B-spline methods, the effect of varying control point grid spacing, image subsampling rate, regularization method, and its corresponding weights, are studied. The effect of Gaussian kernel width used to smooth the displacement field is explored for the demons algorithm. Experimental evaluation is performed on the Lung CT Segmentation Challenge (LCTSC) dataset.
