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

doi:10.1117/12.877866

11 March 2011 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

Author Affiliations +

Proceedings Volume 7962, Medical Imaging 2011: Image Processing; 79621B (2011) https://doi.org/10.1117/12.877866
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

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.

Citation Download Citation

Kaikai Shen, Pierrick Bourgeat, Fabrice Meriaudeau, and Olivier Salvado "Atlas selection strategy in multi-atlas segmentation propagation with locally weighted voting using diversity-based MMR re-ranking", Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79621B (11 March 2011); https://doi.org/10.1117/12.877866

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