title: MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection.
abstract: Atlas-based automated anatomical labeling is a fundamental tool in medical image segmentation, as it defines regions of interest for subsequent analysis of structural and functional image data. The extensive investigation of multi-atlas warping and fusion techniques over the past 5 or more years has clearly demonstrated the advantages of consensus-based segmentation. However, the common approach is to use multiple atlases with a single registration method and parameter set, which is not necessarily optimal for every individual scan, anatomical region, and problem/data-type. Different registration criteria and parameter sets yield different solutions, each providing complementary information. Herein, we present a consensus labeling framework that generates a broad ensemble of labeled atlases in target image space via the use of several warping algorithms, regularization parameters, and atlases. The label fusion integrates two complementary sources of information: a local similarity ranking to select locally optimal atlases and a boundary modulation term to refine the segmentation consistently with the target image's intensity profile. The ensemble approach consistently outperforms segmentations using individual warping methods alone, achieving high accuracy on several benchmark datasets. The MUSE methodology has been used for processing thousands of scans from various datasets, producing robust and consistent results. MUSE is publicly available both as a downloadable software package, and as an application that can be run on the CBICA Image Processing Portal (https://ipp.cbica.upenn.edu), a web based platform for remote processing of medical images.
authors.affiliation: Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
authors.forename: Jimit
authors.initials: J
authors.lastname: Doshi
authors.affiliation: Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
authors.forename: Guray
authors.initials: G
authors.lastname: Erus
authors.affiliation: Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.|Martinos Biomedical Imaging Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02129.
authors.forename: Yangming
authors.initials: Y
authors.lastname: Ou
authors.affiliation: Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland, USA.
authors.forename: Susan M
authors.initials: SM
authors.lastname: Resnick
authors.affiliation: Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA.
authors.forename: Ruben C
authors.initials: RC
authors.lastname: Gur
authors.affiliation: Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA.
authors.forename: Raquel E
authors.initials: RE
authors.lastname: Gur
authors.affiliation: Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA.
authors.forename: Theodore D
authors.initials: TD
authors.lastname: Satterthwaite
authors.affiliation: Division of Nephrology, Childrens Hospital of Philadelphia, 34th and Civic Center Boulevard, Philadelphia PA, USA.
authors.forename: Susan
authors.initials: S
authors.lastname: Furth
authors.affiliation: Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
authors.forename: Christos
authors.initials: C
authors.lastname: Davatzikos
mesh_terms: D000465:Algorithms; D000716:Anatomy, Artistic; D001271:Atlases as Topic; D001921:Brain / Q000033:anatomy & histology*; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods
keywords: Label fusion; MRI; Multi-atlas segmentation; ROI; Registration