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Automatic Segmentation of Different Pathologies from Cardiac Cine MRI Using Registration and Multiple Component EM Estimation

  • Wenzhe Shi
  • Xiahai Zhuang
  • Haiyan Wang
  • Simon Duckett
  • Declan Oregan
  • Philip Edwards
  • Sebastien Ourselin
  • Daniel Rueckert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6666)

Abstract

In this paper, we develop a framework for the automatic detection and segmentation of the ventricle and myocardium from multi-slice, short-axis cine MR images. The segmentation framework has the ability to deal with large shape variability of the heart, poorly defined boundaries and abnormal intensity distribution of the myocardium (e.g. due to infarcts). We integrate a series of state-of-the-art techniques into a fully automatic workflow, including a detection algorithm for the LV, atlas-based segmentation, and intensity-based refinement using a Gaussian mixture model that is optimized using the Expectation Maximization (EM) algorithm and the graph cut algorithm. We evaluate this framework on three different patient groups, one with infarction, one with left ventricular hypertrophy (both are common result of cardiovascular diseases) and another group of subjects with normal heart anatomy. Results indicate that the proposed method is capable of producing segmentation results that show good robustness and high accuracy (Dice 0.908±0.025 for the endocardial and 0.946±0.016 for the epicardial segmentations) across all patient groups with and without pathology.

Keywords

Expectation Maximiza Gaussian Mixture Model Automatic Segmentation Expectation Maximiza Algorithm Probabilistic Atlas 
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.
    Huang, S., Liu, J., Lee, L., Venkatesh, S., Teo, L., Au, C., Nowinski, W.: An image-based comprehensive approach for automatic segmentation of left ventricle from cardiac short axis cine mr images. Journal of Digital Imaging, 1–11 (2010) 10.1007/s10278-010-9315-4Google Scholar
  2. 2.
    Pluempitiwiriyawej, C., Moura, J., Wu, Y., Ho, C.: STACS: New active contour scheme for cardiac MR image segmentation. IEEE Transactions on Medical Imaging 24(5), 593–603 (2005)CrossRefGoogle Scholar
  3. 3.
    Kaus, M., Berg, J., Weese, J., Niessen, W., Pekar, V.: Automated segmentation of the left ventricle in cardiac MRI. Medical Image Analysis 8(3), 245–254 (2004)CrossRefGoogle Scholar
  4. 4.
    Rouchdy, Y., Pousin, J., Schaerer, J., Clarysse, P.: A nonlinear elastic deformable template for soft structure segmentation. Inverse Problems 23, 1017–1035 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Lorenzo-Valdés, M., Sanchez-Ortiz, G., Elkington, A., Mohiaddin, R., Rueckert, D.: Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm. Medical Image Analysis 8(3), 255–265 (2004)CrossRefGoogle Scholar
  6. 6.
    Zhuang, X., Rhode, K.S., Arridge, S.R., Razavi, R.S., Hill, D., Hawkes, D.J., Ourselin, S.: An atlas-based segmentation propagation framework using locally affine registration – application to automatic whole heart segmentation. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part II. LNCS, vol. 5242, pp. 425–433. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Zhuang, X., 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, 1612–1625 (2010)Google Scholar
  8. 8.
    Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging 20(1), 45–57 (2001)CrossRefGoogle Scholar
  9. 9.
    Kedenburg, G., Cocosco, C.: Automatic cardiac MRI myocardium segmentation using graphcut. In: Proceedings of SPIE, vol. 6144, 61440A (2006) Google Scholar
  10. 10.
    Boykov, Y., Kolmogorov, V.: Computing Geodesics and Minimal Surfaces via Graph Cuts. In: Proceedings of the Ninth IEEE International Conference on Computer Vision, p. 26. IEEE Computer Society, Los Alamitos (2003)CrossRefGoogle Scholar
  11. 11.
    Jolly, M.: Automatic segmentation of the left ventricle in cardiac MR and CT images. International Journal of Computer Vision 70(2), 151–163 (2006)CrossRefGoogle Scholar
  12. 12.
    Ashburner, J., Friston, K.J.: Unified segmentation. NeuroImage 26(3), 839–851 (2005)CrossRefGoogle Scholar
  13. 13.
    Viola, P., Jones, M.: Robust real-time object detection. International Journal of Computer Vision 57(2), 137–154 (2002)CrossRefGoogle Scholar
  14. 14.
    Greig, D., Porteous, B., Seheult, A.: Exact maximum a posteriori estimation for binary images. Journal of the Royal Statistical Society. Series B (Methodological) 51(2), 271–279 (1989)Google Scholar
  15. 15.
    Khan, S.M., Shah, M.: A multiview approach to tracking people in crowded scenes using a planar homography constraint. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 133–146. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Lu, Y., Radau, P., Connelly, K., Dick, A., Wright, G.A.: Segmentation of Left Ventricle in Cardiac Cine MRI: An Automatic Image-Driven Method. In: Ayache, N., Delingette, H., Sermesant, M. (eds.) FIMH 2009. LNCS, vol. 5528, pp. 339–347. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  17. 17.
    Huang, S., Liu, J.: Segmentation of the Left Ventricle from Cine MR Images Using a Comprehensive Approach. In: Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart, pp. 339–347. Springer, Heidelberg (2009)Google Scholar
  18. 18.
    Grevera, G., Udupa, J.: Shape-based interpolation of multidimensional grey-level images. IEEE Transactions on Medical Imaging 15(6), 881–892 (2002)CrossRefGoogle Scholar
  19. 19.
    Lorensen, W., Cline, H.: Marching cubes: A high resolution 3D surface construction algorithm. In: Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques, pp. 163–169. ACM, New York (1987)Google Scholar
  20. 20.
    Schaerer, J., Casta, C., Pousin, J., Clarysse, P.: A dynamic elastic model for segmentation and tracking of the heart in mr image sequences. Medical Image Analysis 14(6), 738–749 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Wenzhe Shi
    • 1
  • Xiahai Zhuang
    • 2
  • Haiyan Wang
    • 1
  • Simon Duckett
    • 3
  • Declan Oregan
    • 4
  • Philip Edwards
    • 1
  • Sebastien Ourselin
    • 2
  • Daniel Rueckert
    • 1
  1. 1.Biomedical Image Analysis GroupImperial College LondonUK
  2. 2.Centre for Medical Image ComputingUniversity College LondonUK
  3. 3.The Rayne InstituteKings College LondonUK
  4. 4.Robert Steiner MRI UnitHammersmith HospitalUK

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