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Incorporating Shape Variability in Image Segmentation via Implicit Template Deformation

  • Raphael Prevost
  • Remi Cuingnet
  • Benoit Mory
  • Laurent D. Cohen
  • Roberto Ardon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8151)

Abstract

Implicit template deformation is a model-based segmentation framework that was successfully applied in several medical applications. In this paper, we propose a method to learn and use prior knowledge on shape variability in such framework. This shape prior is learnt via an original and dedicated process in which both an optimal template and principal modes of variations are estimated from a collection of shapes. This learning strategy requires neither a pre-alignment of the training shapes nor one-to-one correspondences between shape sample points. We then generalize the implicit template deformation formulation to automatically select the most plausible deformation as a shape prior. This novel framework maintains the two main properties of implicit template deformation: topology preservation and computational efficiency. Our approach can be applied to any organ with a possibly complex shape but fixed topology. We validate our method on myocardium segmentation from cardiac magnetic resonance short-axis images and demonstrate segmentation improvement over standard template deformation.

Keywords

Image Segmentation Regularization Term Residual Deformation Shape Constraint Shape Variability 
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-Verlag Berlin Heidelberg 2013

Authors and Affiliations

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

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