Atlas selection strategy in multi-atlas segmentation propagation with locally weighted voting using diversity-based MMR re-ranking

Kaikai Shen*, Pierrick Bourgeat, Fabrice Meriaudeau, Olivier Salvado, Alzheimer's Disease Neuroimaging Initiative

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

1 Citation (Scopus)


In multi-atlas based image segmentation, multiple atlases with label maps are propagated to the query image, and fused into the segmentation result. Voting rule is commonly used classifier fusion method to produce the consensus map. Local weighted voting (LWV) is another method which combines the propagated atlases weighted by local image similarity. When LWV is used, we found that the segmentation accuracy converges slower comparing to simple voting rule. We therefore propose to introduce diversity in addition to image similarity by using Maximal Marginal Relevance (MMR) criteria as a more efficient way to rank and select atlases. We test the MMR re-ranking on a hippocampal atlas set of 138 normal control (NC) subjects and another set of 99 Alzheimer's disease patients provided by ADNI. The result shows that MMR re-ranking performed better than similarity based atlas selection when same number of atlases were selected.

Original languageEnglish
Title of host publicationMedical Imaging 2011
Subtitle of host publicationImage Processing
EditorsBenoit M. Dawant, David R. Haynor
Place of PublicationWashington
Number of pages6
ISBN (Electronic)9780819485045
Publication statusPublished - 2011
Externally publishedYes
EventConference on Medical Imaging 2011 - Image Processing - Lake Buena Vista
Duration: 14 Feb 201116 Feb 2011

Publication series

NameProceedings of SPIE
ISSN (Print)0277-786X


ConferenceConference on Medical Imaging 2011 - Image Processing
CityLake Buena Vista


  • MRI
  • image segmentation
  • multi-atlas segmentation propagation
  • MMR
  • atlas selection
  • locally weighted voting

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