Incorporating Shape Variability in Image Segmentation via Implicit Template Deformation


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.

DOI: 10.1007/978-3-642-40760-4_11

Cite this paper

@article{Prevost2013IncorporatingSV, title={Incorporating Shape Variability in Image Segmentation via Implicit Template Deformation}, author={Raphael Prevost and R{\'e}mi Cuingnet and Benoit Mory and Laurent D. Cohen and Roberto Ardon}, journal={Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention}, year={2013}, volume={16 Pt 3}, pages={82-9} }