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A Multi-image Graph Cut Approach for Cardiac Image Segmentation and Uncertainty Estimation

  • Wenzhe Shi
  • Xiahai Zhuang
  • Robin Wolz
  • Duckett Simon
  • KaiPin Tung
  • Haiyan Wang
  • Sebastien Ourselin
  • Philip Edwards
  • Reza Razavi
  • Daniel Rueckert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7085)

Abstract

Registration and segmentation uncertainty may be important information to convey to a user when automatic image analysis is performed. Uncertainty information may be used to provide additional diagnostic information to traditional analysis of cardiac function. In this paper, we develop a framework for the automatic segmentation of the cardiac anatomy from multiple MR images. We also define the registration and segmentation uncertainty and explore its use for diagnostic purposes. Our framework uses cardiac MR image sequences that are widely available in clinical practice. We improve the performance of the cardiac segmentation algorithms by combining information from multiple MR images using a graph-cut based segmentation. We evaluate this framework on images from 32 subjects: 13 patients with ischemic cardiomyopathy, 14 patients with dilated cardiomyopathy and 5 normal volunteers. Our results indicate that the proposed method is capable of producing segmentation results with very high robustness and high accuracy with minimal user interaction across all subject groups. We also show that registration and segmentation uncertainties are good indicators for segmentation failures as well as good predictors for the functional abnormality of the subject.

Keywords

Multiple Image Cardiac Anatomy Cine Magnetic Resonance Image Smoothness Term 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.
    Uribe, S., Muthurangu, V., et al.: Whole-heart cine MRI using real-time respiratory self-gating. Magnetic Resonance in Medicine 57(3), 606–613 (2007)CrossRefGoogle Scholar
  2. 2.
    Lorenzo-Valdés, M., Sanchez-Ortiz, G., Rueckert, D., et al.: Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm. Medical Image Analysis 8(3), 255–265 (2004)CrossRefGoogle Scholar
  3. 3.
    Zhuang, X., Rhode, K., Ourselin, S., et al.: A Registration-Based Propagation Framework for Automatic Whole Heart Segmentation of Cardiac MRI. IEEE Transactions on Medical Imaging, 1612–1625 (2010)Google Scholar
  4. 4.
    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
  5. 5.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1222–1239 (2001)Google Scholar
  6. 6.
    Risholm, P., Pieper, S., Samset, E., Wells III, W.M.: Summarizing and Visualizing Uncertainty in Non-rigid Registration. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part II. LNCS, vol. 6362, pp. 554–561. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Kybic, J.: Bootstrap resampling for image registration uncertainty estimation without ground truth. IEEE Transactions on Image Processing 19(1), 64–73 (2009)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Shi, W., Zhuang, X., Wang, H., Duckett, S., Oregan, D., Edwards, P., Ourselin, S., Rueckert, D.: Automatic Segmentation of Different Pathologies from Cardiac Cine MRI Using Registration and Multiple Component EM Estimation. In: Metaxas, D.N., Axel, L. (eds.) FIMH 2011. LNCS, vol. 6666, pp. 163–170. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Chandler, A., Razavi, R., et al.: Correction of misaligned slices in multi-slice MR cardiac examinations by using slice-to-volume registration. In: 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, pp. 474–477. IEEE (2006)Google Scholar
  10. 10.
    Linguraru, M.G., Pura, J.A., Chowdhury, A.S., Summers, R.M.: Multi-organ Segmentation from Multi-phase Abdominal CT via 4D Graphs Using Enhancement, Shape and Location Optimization. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part III. LNCS, vol. 6363, pp. 89–96. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Wolz, R., Heckemann, R., Aljabar, P., et al.: Measurement of hippocampal atrophy using 4D graph-cut segmentation: Application to ADNI. NeuroImage (2010)Google Scholar
  12. 12.
    Rohlfing, T., Maurer Jr., C., Bluemke, D., Jacobs, M.: Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint. IEEE Transactions on Medical Imaging 22(6), 730–741 (2003)CrossRefGoogle Scholar
  13. 13.
    Koay, C., Basser, P.: Analytically exact correction scheme for signal extraction from noisy magnitude MR signals. Journal of Magnetic Resonance 179(2), 317–322 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Wenzhe Shi
    • 1
  • Xiahai Zhuang
    • 2
  • Robin Wolz
    • 1
  • Duckett Simon
    • 3
  • KaiPin Tung
    • 1
  • Haiyan Wang
    • 1
  • Sebastien Ourselin
    • 2
  • Philip Edwards
    • 1
  • Reza Razavi
    • 3
  • Daniel Rueckert
    • 1
  1. 1.Biomedical Image Analysis Group, Department of ComputingImperial College LondonUK
  2. 2.Centre for Medical Image Computing, Department of ComputingUniversity College LondonUK
  3. 3.The Rayne InstituteKings College LondonUK

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