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Publication Detail
Multi-STEPS: Multi-label similarity and truth estimation for propagated segmentations
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Conference Proceeding
  • Authors:
    Cardoso MJ, Modat M, Ourselin S, Keihaninejad S, Cash D
  • Publication date:
    24/04/2012
  • Pagination:
    153, 158
  • Journal:
    Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis
  • Status:
    Published
Abstract
Quantitative analysis in medical imaging often relies on anatomical segmentation of MR or CT images. Several multi-atlas based segmentation propagation methods have recently been published due to the accurate structural segmentations produced by propagating and combining manual delineations from multiple templates in a database. We propose a new multi-label local ranking strategy for template selection based on the locally normalised cross correlation (LNCC) and an extension to the classical STAPLE algorithm by Warfield et al. [15]. It addresses the known problem of local vs. global image matching and the bias introduced in the performance estimation due to structure size. Results show a significant improvement in terms of segmentation accuracy for key brain areas when compared to state-of-the-art brain parcelation algorithms and reduced discontinuity and fragmentation between structures due to the MRF derived smoothness. © 2012 IEEE.
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