Alzheimer's disease (AD) severely affects the hippocampus: it loses mass and shrinks as the disease advances.
Thus delineation of the hippocampus is an important task in the clinical study of AD. Because of its simplicity
and good performance, multi-atlas based segmentation has become a popular approach for medical image
segmentation. We propose to use manifold learning for atlas selection in the framework of multi-atlas based
segmentation. The framework only benefits when selecting atlases similar to the target image. Since manifold
learning assigns each image a coordinate in low-dimensional space by respecting the neighborhood relationship,
it is well suited for atlas selection. The key contribution is that we use manifold learning based on a metric
derived from non-rigid transformation as the resulting embedding better captures deformations or shape differences
between images than similarity measures based on voxel intensity. The proposed method is evaluated in
a leave-one-out experiment on a set of 110 hippocampus images; we report mean Dice score of 0.9114 (0.0227).
The method was validated against a state-of-the-art method for hippocampus segmentation.