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Automatic Delineation of Left and Right Ventricles in Cardiac MRI Sequences Using a Joint Ventricular Model

  • Xiaoguang Lu
  • Yang Wang
  • Bogdan Georgescu
  • Arne Littman
  • Dorin Comaniciu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6666)

Abstract

Cardiac magnetic resonance imaging (MRI) has advanced to become a powerful tool in clinical practice. Extraction of morphological and functional features from cardiac MR imaging for diagnosis and disease monitoring remains a time-consuming task for clinicians. We present a fully automatic approach to extracting the structures and dynamics for both left and right ventricles. The cine short-axis stack of a cardiac MR scan is used to reconstruct a 3D volume sequence. A joint LV-RV model is introduced to delineate the boundaries of both left and right ventricles in each frame, and to combine both spatial and temporal context to track the chamber boundary motion over cardiac cycles. Both qualitative and quantitative results show promise of the proposed method.

Keywords

Right Ventricle Cardiac Magnetic Resonance Imaging Boundary Detection Right Ventricle Function Neighboring Frame 
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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References

  1. 1.
    Frangi, A., Niessen, W., Viergever, M.: Three-dimensional modeling for functional analysis of cardiac images: A review. IEEE Trans. on Medical Imaging 20(1) (2001)Google Scholar
  2. 2.
    Grothues, F., Moon, J., Bellenger, N., Smith, G., Klein, H., Pennell, D.: Interstudy reproducibility of right ventricular volumes, function, and mass with cardiovascular magnetic resonance. American Heart Journal 147(2), 218–223 (2004)CrossRefGoogle Scholar
  3. 3.
    Corsi, C., Caiani, E., Catalano, O., Antonaci, S., Veronesi, F., Sarti, A., Lamberti, C.: Improved quantification of right ventricular volumes from cardiac magnetic resonance data. Computers in Cardiology 57(2), 37–40 (2005)Google Scholar
  4. 4.
    Haber, E., Metaxas, D., Axel, L., Wells, W., Colchester, A., Delp, S.: Motion analysis of the right ventricle from MRI images. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, p. 177. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  5. 5.
    Sun, H., Frangi, A.F., Wang, H., Sukno, F.M., Tobon-Gomez, C., Yushkevich, P.A.: Automatic cardiac MRI segmentation using a biventricular deformable medial model. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6361, pp. 468–475. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Tu, Z.: Probabilistic boosting-tree: Learning discriminative models for classification, recognition, and clustering. In: Proc. ICCV, pp. 1589–1596 (2005)Google Scholar
  7. 7.
    Viola, P., Jones, M.J.: Robust real-time face detection. International Journal of Computer Vision 57(2), 137–154 (2004)CrossRefGoogle Scholar
  8. 8.
    Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features. IEEE Transactions on Medical Imaging 27(11), 1668–1681 (2008)CrossRefGoogle Scholar
  9. 9.
    Peters, J., Ecabert, O., Meyer, C., Kneser, R., Weese, J.: Optimizing boundary detection via simulated search with applications to multi-modal heart segmentation. Medical Image Analysis 14(1), 70–84 (2010)CrossRefGoogle Scholar
  10. 10.
    Zhuang, X., Leung, K., Rhode, K., Razavi, R., Hawkes, D.J., Ourselin, S.: A registration-based propagation framework for automatic whole heart segmentation of cardiac mri. IEEE Transactions on Medical Imaging 29(9), 1612–1625 (2010)CrossRefGoogle Scholar
  11. 11.
    Yang, L., Georgescu, B., Zheng, Y., Meer, P., Comaniciu, D.: 3D ultrasound tracking of the left ventricles using one-step forward prediction and data fusion of collaborative trackers. In: CVPR (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Xiaoguang Lu
    • 1
  • Yang Wang
    • 1
  • Bogdan Georgescu
    • 1
  • Arne Littman
    • 2
  • Dorin Comaniciu
    • 1
  1. 1.Siemens Corporate ResearchPrincetonUSA
  2. 2.Magnetic Resonance, Siemens HealthcareErlangenGermany

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