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Segmentation of Right Ventricle in Cardiac MR Images Using Shape Regression

  • Suman Sedai
  • Pallab Roy
  • Rahil Garnavi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9352)

Abstract

Accurate and automatic segmentation of the right ventricle is challenging due to its complex anatomy and large shape variation observed between patients. In this paper the ability of shape regression is explored to segment right ventricle in presence of large shape variation among the patients. We propose a robust and efficient cascaded shape regression method which iteratively learns the final shape from a given initial shape. We use gradient boosted regression trees to learn each regressor in the cascade to take the advantage of supervised feature selection mechanism. A novel data augmentation method is proposed to generate synthetic training samples to improve regressors performance. In addition to that, a robust fusion method is proposed to reduce the the variance in the predictions given by different initial shapes, which is a major drawback of cascade regression based methods. The proposed method is evaluated on an image set of 45 patients and shows high segmentation accuracy with dice metric of \(0.87\pm 0.06\). Comparative study shows that our proposed method performs better than state-of-the-art multi-atlas label fusion based segmentation methods.

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References

  1. 1.
    Petitjean, C., Zuluaga, M.A., Bai, W., Dacher, J.-N., Grosgeorge, D., Jérôme Caudron, S., Ruan, I.B., Ayed, M.J., Cardoso, H.-C.C., et al.: Right ventricle segmentation from cardiac MRI: A collation study. Medical image analysis 19(1), 187–202 (2015)CrossRefGoogle Scholar
  2. 2.
    Gering, D.T.: Automatic segmentation of cardiac MRI. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2878, pp. 524–532. Springer, Heidelberg (2003) CrossRefGoogle Scholar
  3. 3.
    Mahapatra, D., Buhmann, J.M.: Cardiac LV and RV segmentation using mutual context information. In: Wang, F., Shen, D., Yan, P., Suzuki, K. (eds.) MLMI 2012. LNCS, vol. 7588, pp. 201–209. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  4. 4.
    Mitchell, S.C., Lelieveldt, B.P.F., van der Geest, R.J., Bosch, H.G., Reiver, J.H.C., Sonka, M.: Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac mr images. IEEE Transactions on Medical Imaging 20(5), 415–423 (2001)CrossRefGoogle Scholar
  5. 5.
    Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models-their training and application. Comput. Vis. Image Underst. 61(1), 38–59 (1995)CrossRefGoogle Scholar
  6. 6.
    Ou, Y., Doshi, J., Erus, G., Davatzikos, C.: Multi-atlas segmentation of the cardiac MR right ventricle. In: RV Segmentation Challenge at MICCAI (2012)Google Scholar
  7. 7.
    Zhou, S.K.: Shape regression machine and efficient segmentation of left ventricle endocardium from 2D B-mode echocardiogram. Medical Image Analysis 14(4), 563–581 (2010)CrossRefGoogle Scholar
  8. 8.
    Dollár, P., Welinder, P., Perona, P.: Cascaded pose regression. In: CVPR, pp. 1078–1085. IEEE (2010)Google Scholar
  9. 9.
    Cao, X., Wei, Y., Wen, F., Sun, J.: Face alignment by explicit shape regression. IJCV 107(2), 177–190 (2014)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Friedman, J.H.: Greedy function approximation: A gradient boosting machine. Annals of Statistics 29, 1189–1232 (2000)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Radau, P., Lu, Y., Connelly, K., Paul, G., Dick, A., Wright, G.: Evaluation framework for algorithms segmenting short axis cardiac MRI. The MIDAS Journal 49 (2009)Google Scholar
  12. 12.
    Sabuncu, M.R., Yeo, B.T., Van Leemput, K., Fischl, B., Golland, P.: A generative model for image segmentation based on label fusion. IEEE Trans. Med. Imaging 29(10), 1714–1729 (2010)CrossRefGoogle Scholar
  13. 13.
    Wang, H., Suh, J.W., Das, S.R., Pluta, J.B., Craige, C., Yushkevich, P.A.: Multi-atlas segmentation with joint label fusion. IEEE Transactions on PAMI 35(3), 611–623 (2013)CrossRefGoogle Scholar
  14. 14.
    Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.W.: Elastix: a toolbox for intensity-based medical image registration. IEEE Transactions on Medical Imaging 29(1), 196–205 (2010)CrossRefGoogle Scholar

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© Springer International Publishing Switzerland 2015

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Authors and Affiliations

  1. 1.IBM Research AustraliaCarltonAustralia

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