Using manifold learning for atlas selection in multi-atlas segmentation.

ABSTRACT: Multi-atlas segmentation has been widely used to segment various anatomical structures. The success of this technique partly relies on the selection of atlases that are best mapped to a new target image after registration. Recently, manifold learning has been proposed as a method for atlas selection. Each manifold learning technique seeks to optimize a unique objective function. Therefore, different techniques produce different embeddings even when applied to the same data set. Previous studies used a single technique in their method and gave no reason for the choice of the manifold learning technique employed nor the theoretical grounds for the choice of the manifold parameters. In this study, we compare side-by-side the results given by 3 manifold learning techniques (Isomap, Laplacian E

SUBMITTER: Hoang Duc AK 

PROVIDER: S-EPMC3732273 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature