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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)

Abstract

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 http://picsl.upenn.edu/Project/SunMiccai2010 .

Keywords

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.

References

  1. 1.
    van Assen, H.C., Danilouchkine, M.G., Dirksen, M.S., Reiber, J.C., Lelieveldt, B.F.: A 3-D active shape model driven by fuzzy inference: Application to cardiac CT and MR. IEEE T. Inf. Technol. B 12(5), 595–605 (2008)CrossRefGoogle Scholar
  2. 2.
    Blum, H., Nagel, R.: Shape description using weighted symmetric axis features. Pattern Recognit. 10(3), 167–180 (1978)zbMATHCrossRefGoogle Scholar
  3. 3.
    Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape models - their training and application. Comput. Vis. Image Underst. 1(61), 38–59 (1994)Google Scholar
  4. 4.
    Cristinacce, D., Cootes, T.: Facial feature detection using adaboost with shape constraints. In: British Machine Vision Conference, vol. 1, pp. 231–240 (2003)Google Scholar
  5. 5.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Jolly, M.P., Xue, H., Grady, L., Guehring, J.: Combining registration and minimum surfaces for the segmentation of the left ventricle in cardiac cine MR images. Med. Image Comput. Comput. Assist Interv. 12(Pt 2), 910–918 (2009)Google Scholar
  7. 7.
    Koenderink, J.J., van Doorn, A.J.: Representation of local geometry in the visual system. Biol. Cybern. 55(6), 367–375 (1987)zbMATHCrossRefGoogle Scholar
  8. 8.
    Loop, C., DeRose, T.: Generalized b-spline surfaces of arbitrary topology. In: Computer Graphics (ACM SIGGRAPH Proceedings), pp. 347–356 (1990)Google Scholar
  9. 9.
    Lorenzo-Valdés, M., Sanchez-Ortiz, G.I., Elkington, A.G., Mohiaddin, R.H., Rueckert, D.: Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm. Med. Image Anal. 8(3), 255–265 (2004)CrossRefGoogle Scholar
  10. 10.
    Lotjonen, J., Kivisto, S., Koikkalainen, J., Smutek, D., Lauerma, K.: Statistical shape model of atria, ventricles and epicardium from short- and long-axis MR images. Med. Image Anal. 8(3), 371–386 (2004)CrossRefGoogle Scholar
  11. 11.
    Mitchell, S.C., Bosch, J.G., Lelieveldt, B.P.F., van der Geest, R.J., Reiber, J.H.C., Sonka, M.: 3-D active appearance models: segmentation of cardiac MR and ultrasound images. IEEE T. Med. Imaging 21(9), 1167–1178 (2002)CrossRefGoogle Scholar
  12. 12.
    Paragios, N.: A Variational Approach for the Segmentation of the Left Ventricle in Cardiac Image Analysis. Int. J. Comput Vision 50(3), 345–362 (2002)zbMATHCrossRefGoogle Scholar
  13. 13.
    Peters, J., Ecabert, O., Meyer, C., Kneser, R., Weese, J.: Optimizing boundary detection via simulated search with applications to multi-modal heart segmentation. Med. Image Anal. 14(1), 70–84 (2010)CrossRefGoogle Scholar
  14. 14.
    Sun, H., Avants, B.B., Frangi, A.F., Sukno, F., Gee, J.C., Yushkevich, P.A.: Cardiac medial modeling and time-course heart wall thickness analysis. Med. Image Comput. Comput. Assist Interv. 11(Pt. 2), 766–773 (2008)Google Scholar
  15. 15.
    Yushkevich, P.A., Zhang, H., Gee, J.: Continuous medial representation for anatomical structures. IEEE T. Med. Imaging 25(2), 1547–1564 (2006)CrossRefGoogle Scholar
  16. 16.
    Zeng, X., Staib, L., Schultz, R., Duncan, J.: Volumetric layer segmentation using coupled surfaces propagation. In: Proc. CVPR IEEE, pp. 708–715 (1998)Google Scholar
  17. 17.
    Zhuang, X., Rhode, K., Arridge, S., Razavi, R., Hill, D., Hawkes, D., Ourselin, S.: An atlas-based segmentation propagation framework locally affine registration–application to automatic whole heart segmentation. Med. Image Comput. Comput. Assist Interv. 11(Pt 2), 425–433 (2008)Google Scholar

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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