MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection.

New CAR Research Sheds Light on


Universal Screening for Autism in Toddlers

TitleMUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection.
Publication TypeJournal Article
Year of Publication2016
AuthorsDoshi, J, Erus, G, Ou, Y, Resnick, SM, Gur, RC, Gur, RE, Satterthwaite, TD, Furth, S, Davatzikos, C
Corporate AuthorsAlzheimer's Neuroimaging Initiative
Date Published2016 Feb 15
KeywordsAlgorithms, Anatomy, Artistic, Atlases as Topic, Brain, Brain Mapping, Humans, Image Processing, Computer-Assisted, Pattern Recognition, Automated

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 (, a web based platform for remote processing of medical images.

Alternate JournalNeuroimage
PubMed ID26679328
PubMed Central IDPMC4806537
Grant ListHHSN271201300284 / / PHS HHS / United States
P50 AG005146 / AG / NIA NIH HHS / United States
HHSN271201300284P / DA / NIDA NIH HHS / United States
/ / Intramural NIH HHS / United States
R01 AG014971 / AG / NIA NIH HHS / United States
Leave a Comment