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Tagged Template Deformation

  • Raphael Prevost
  • Rémi Cuingnet
  • Benoit Mory
  • Laurent D. Cohen
  • Roberto Ardon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

Abstract

Model-based approaches are very popular for medical image segmentation as they carry useful prior information on the target structure. Among them, the implicit template deformation framework recently bridged the gap between the efficiency and flexibility of level-set region competition and the robustness of atlas deformation approaches. This paper generalizes this method by introducing the notion of tagged templates. A tagged template is an implicit model in which different subregions are defined. In each of these subregions, specific image features can be used with various confidence levels. The tags can be either set manually or automatically learnt via a process also hereby described. This generalization therefore greatly widens the scope of potential clinical application of implicit template deformation while maintaining its appealing algorithmic efficiency. We show the great potential of our approach in myocardium segmentation of ultrasound images.

Keywords

Ground Truth Random Forest Regularization Term Appearance Model Active Appearance Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Raphael Prevost
    • 1
    • 2
  • Rémi Cuingnet
    • 1
  • Benoit Mory
    • 1
  • Laurent D. Cohen
    • 2
  • Roberto Ardon
    • 1
  1. 1.Philips Research MedisysSuresnesFrance
  2. 2.CEREMADE UMR 7534, Universite Paris DauphineParisFrance

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