Automatic Cardiac MRI Segmentation Using a Biventricular Deformable Medial Model

  • Hui Sun
  • Alejandro F. Frangi
  • Hongzhi Wang
  • Federico M. Sukno
  • Catalina Tobon-Gomez
  • Paul A. Yushkevich
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6361)


We present a novel approach for automatic segmentation of the myocardium in short-axis MRI using deformable medial models with an explicit representation of thickness. Segmentation is constrained by a Markov prior on myocardial thickness. Best practices from Active Shape Modeling (global PCA shape prior, statistical appearance model, local search) are adapted to the medial model. Segmentation performance is evaluated by comparing to manual segmentation in a heterogeneous adult MRI dataset. Average boundary displacement error is under 1.4 mm for left and right ventricles, comparing favorably with published work.

Additional material can be found at .


Local Search Right Ventricle Automatic Segmentation Manual Segmentation 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-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Hui Sun
    • 1
  • Alejandro F. Frangi
    • 2
    • 3
  • Hongzhi Wang
    • 1
  • Federico M. Sukno
    • 2
    • 3
  • Catalina Tobon-Gomez
    • 2
    • 3
  • Paul A. Yushkevich
    • 1
  1. 1.Penn Image Computing and Science Laboratory (PICSL), Department of RadiologyUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Center for Computational Imaging & Simulation Technologies in BiomedicineUniversitat Pompeu FabraBarcelonaSpain
  3. 3.Centro de Investigación Biomédica en Red en BioingenieríaBiomateriales y Nanomedicina (CIBER-BBN)ZaragozaSpain

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