Automated Model-Based Segmentation of the Left and Right Ventricles in Tagged Cardiac MRI

  • Albert Montillo
  • Dimitris Metaxas
  • Leon Axel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2878)


We describe an automated, model-based method to segment the left and right ventricles in 4D tagged MR. We fit 3D epicardial and endocardial surface models to ventricle features we extract from the image data. Excellent segmentation is achieved using novel methods that (1) initialize the models and (2) that compute 3D model forces from 2D tagged MR images. The 3D forces guide the models to patient-specific anatomy while the fit is regularized via internal deformation strain energy of a thin plate. Deformation continues until the forces equilibrate or vanish. Validation of the segmentations is performed quantitatively and qualitatively on normal and diseased subjects.


Right Ventricle Deformable Model Image Force Short Axis Image Endocardial Surface 
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  1. 1.
    Axel, L., Dougherty, L.: Heart wall motion: improved method of spatial modulation of magnetization for MR imaging. Radiology 172, 349–350 (1989)Google Scholar
  2. 2.
    Zerhouni, E., Parish, D., Rogers, W., et al.: Human heart: tagging with MR imaging-a method for non-invasive assessment of myocardial motion. Radiology 169, 59–63 (1988)Google Scholar
  3. 3.
    Xu, C., Prince, J.: Generalized GVF external forces for active contours. In: Sig. Proc., vol. 71, pp. 131–139 (1998)Google Scholar
  4. 4.
    Montillo, A., Metaxas, D., Axel, L.: Automated segmentation of the left and right ventricles in 4D cardiac SPAMM images. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2488, p. 620. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    Montillo, A., Axel, L., Metaxas, D.: Automated correction of background intensity variation and img. scale standardization in 4D cardiac SPAMM-MRI. In: Proc. ISMRM (July 2003)Google Scholar
  6. 6.
    Metaxas, D.: Physics-based deformable models: applications to computer vision, graphics, and medical imaging. Kluwer Academic Publishers, Cambridge (1996)Google Scholar
  7. 7.
    Rueckert, D., Burger, P.: Shape-based segmentation and tracking in 4D cardiac MR images. In: Proc. of Med. Img Underst. and Anal., Oxford, UK, July 1997, pp. 193–196 (1997)Google Scholar
  8. 8.
    Saha, P.K., Udupa, J.K.: Scale-based image filtering preserving boundary sharpness and fine structure. IEEE Trans. Med. Img 20, 1140–1155 (2001)CrossRefGoogle Scholar
  9. 9.
    Park, K., Metaxas, D., Axel, L.: LV-RV shape modeling based on a blended parameterized model. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2488, pp. 753–761. Springer, Heidelberg (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Albert Montillo
    • 1
  • Dimitris Metaxas
    • 1
    • 2
  • Leon Axel
    • 3
  1. 1.University of PennsylvaniaPhila.USA
  2. 2.Rutgers UniversityNew BrunswickUSA
  3. 3.New York University, NYUSA

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