Joint segmentation of image ensembles via latent atlases

Tammy Riklin Raviv, Koen Van Leemput, William M. Wells III, Polina Golland

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


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 joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas. Instead, a latent atlas, initialized by a single manual segmentation, is inferred from the evolving segmentations of the ensemble. The proposed method is based on probabilistic principles but is solved using partial differential equations (PDEs) and energy minimization criteria. We evaluate the method by segmenting 50 brain MR volumes. Segmentation accuracy for cortical and subcortical structures approaches the quality of state-of-the-art atlas-based segmentation results, suggesting that the latent atlas method is a reasonable alternative when existing atlases are not compatible with the data to be processed. © 2009 Springer-Verlag.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention
Number of pages9
PublisherSpringer-verlag Berlin
Publication date2009
ISBN (Print)978-3-642-04267-6
Publication statusPublished - 2009
Externally publishedYes
Event12th International Conference on Medical Image Computing and Computer-Assisted Intervention - Imperial College, London, United Kingdom
Duration: 20 Sept 200924 Sept 2009
Conference number: 12


Conference12th International Conference on Medical Image Computing and Computer-Assisted Intervention
LocationImperial College
Country/TerritoryUnited Kingdom
SeriesLecture Notes in Computer Science


  • Differential equations
  • Medical computing
  • Image segmentation


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