title: Segmentation of image ensembles via latent atlases.
abstract: Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. However, the availability of comprehensive, reliable and suitable manual segmentations for atlas construction is limited. We therefore propose a method for joint segmentation of corresponding regions of interest in a collection of aligned images that does not require labeled training data. Instead, a latent atlas, initialized by at most a single manual segmentation, is inferred from the evolving segmentations of the ensemble. The algorithm is based on probabilistic principles but is solved using partial differential equations (PDEs) and energy minimization criteria. We evaluate the method on two datasets, segmenting subcortical and cortical structures in a multi-subject study and extracting brain tumors in a single-subject multi-modal longitudinal experiment. We compare the segmentation results to manual segmentations, when those exist, and to the results of a state-of-the-art atlas-based segmentation method. The quality of the results supports the latent atlas as a promising alternative when existing atlases are not compatible with the images to be segmented.
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA. tammy@csail.mit.edu
authors.forename: Tammy
authors.initials: T
authors.lastname: Riklin-Raviv
authors.forename: Koen
authors.initials: K
authors.lastname: Van Leemput
authors.forename: Bjoern H
authors.initials: BH
authors.lastname: Menze
authors.forename: William M
authors.initials: WM
authors.lastname: Wells
authors.forename: Polina
authors.initials: P
authors.lastname: Golland
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D001932:Brain Neoplasms / Q000473:pathology*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008953:Models, Anatomic; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique