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Proceedings Paper

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

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.

Paper Details

Date Published: 11 March 2011
PDF: 6 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79621B (11 March 2011); doi: 10.1117/12.877866
Show Author Affiliations
Kaikai Shen, Australian e-Health Research Ctr. (Australia)
Le2i-UMR, CNRS, Univ. de Bourgogne (France)
Pierrick Bourgeat, Australian e-Health Research Ctr. (Australia)
Fabrice Meriaudeau, Le2i-UMR, CNRS, Univ. de Bourgogne (France)
Olivier Salvado, Australian e-Health Research Ctr. (Australia)


Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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