Abstract
We propose a novel framework for the automatic propagation of a set of manually labeled brain atlases to a diverse set of images of a population of subjects. A manifold is learned from a coordinate system embedding that allows the identification of neighborhoods which contain images that are similar based on a chosen criterion. Within the new coordinate system, the initial set of atlases is propagated to all images through a succession of multi-atlas segmentation steps. This breaks the problem of registering images that are very "dissimilar" down into a problem of registering a series of images that are "similar". At the same time, it allows the potentially large deformation between the images to be modeled as a sequence of several smaller deformations. We applied the proposed method to an exemplar region centered around the hippocampus from a set of 30 atlases based on images from young healthy subjects and a dataset of 796 images from elderly dementia patients and age-matched controls enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI). We demonstrate an increasing gain in accuracy of the new method, compared to standard multi-atlas segmentation, with increasing distance between the target image and the initial set of atlases in the coordinate embedding, i.e., with a greater difference between atlas and image. For the segmentation of the hippocampus on 182 images for which a manual segmentation is available, we achieved an average overlap (Dice coefficient) of 0.85 with the manual reference.
Original language | English |
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Pages (from-to) | 1316-1325 |
Number of pages | 10 |
Journal | NeuroImage |
Volume | 49 |
Issue number | 2 |
DOIs | |
State | Published - 15 Jan 2010 |
Externally published | Yes |
Keywords
- Atlas-based segmentation
- Coordinate system embedding
- Manifold learning
- Spectral analysis
- Structural MR images