title: LEAP: learning embeddings for atlas propagation.
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.
authors.affiliation: Visual Information Processing Group, Department of Computing, Imperial College London, 180 Queen's Gate, London, SW7 2AZ, UK. r.wolz@imperial.ac.uk
authors.forename: Robin
authors.initials: R
authors.lastname: Wolz
authors.forename: Paul
authors.initials: P
authors.lastname: Aljabar
authors.forename: Joseph V
authors.initials: JV
authors.lastname: Hajnal
authors.forename: Alexander
authors.initials: A
authors.lastname: Hammers
authors.forename: Daniel
authors.initials: D
authors.lastname: Rueckert
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000716:Anatomy, Artistic / Q000379:methods*; D001271:Atlases as Topic; D001331:Automation; D001921:Brain / Q000033:anatomy & histology* / Q000473:pathology; D003704:Dementia / Q000473:pathology; D005260:Female; D006624:Hippocampus / Q000033:anatomy & histology / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009929:Organ Size; D055815:Young Adult