LEAP: Learning embeddings for atlas propagation

Robin Wolz, Paul Aljabar, Jo Hajnal, Alexander Hammers, Daniel Rueckert, Alzheimer's Dis Neuroimaging Initi

Research output: Contribution to journalArticlepeer-review

202 Citations (Scopus)


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 Neurojmaging 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. (C) 2009 Elsevier Inc. All rights reserved.

Original languageEnglish
Pages (from-to)1316-1325
Number of pages10
Issue number2
Publication statusPublished - 15 Jan 2010


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