A Shape-Guided Deformable Model with Evolutionary Algorithm Initialization for 3D Soft Tissue Segmentation

  • Tobias Heimann
  • Sascha Münzing
  • Hans-Peter Meinzer
  • Ivo Wolf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4584)


We present a novel method for the segmentation of volumetric images, which is especially suitable for highly variable soft tissue structures. Core of the algorithm is a statistical shape model (SSM) of the structure of interest. A global search with an evolutionary algorithm is employed to detect suitable initial parameters for the model, which are subsequently optimized by a local search similar to the Active Shape mechanism. After that, a deformable mesh with the same topology as the SSM is used for the final segmentation: While external forces strive to maximize the posterior probability of the mesh given the local appearance around the boundary, internal forces governed by tension and rigidity terms keep the shape similar to the underlying SSM. To prevent outliers and increase robustness, we determine the applied external forces by an algorithm for optimal surface detection with smoothness constraints. The approach is evaluated on 54 CT images of the liver and reaches an average surface distance of 1.6 ±0.5 mm in comparison to manual reference segmentations.


Training Image Shape Model Appearance Model Deformable Model Active Shape 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 Berlin Heidelberg 2007

Authors and Affiliations

  • Tobias Heimann
    • 1
  • Sascha Münzing
    • 1
  • Hans-Peter Meinzer
    • 1
  • Ivo Wolf
    • 1
  1. 1.Div. Medical and Biological Informatics, German Cancer Research Center, 69120 HeidelbergGermany

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