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Direct Estimation of Cardiac Bi-ventricular Volumes with Regression Forests

  • Xiantong Zhen
  • Zhijie Wang
  • Ali Islam
  • Mousumi Bhaduri
  • Ian Chan
  • Shuo Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)

Abstract

Accurate estimation of ventricular volumes plays an essential role in clinical diagnosis of cardiac diseases. Existing methods either rely on segmentation or are restricted to direct estimation of the left ventricle. In this paper, we propose a novel method for direct and joint volume estimation of bi-ventricles, i.e., the left and right ventricles, without segmentation and user inputs. Based on the cardiac image representation by multiple and complementary features, we adopt regression forests to jointly estimate the two volumes. Our method is validated on a dataset of 56 subjects with a total of 3360 MR images which shows that our method can achieve a high correlation coefficient of around 0.9 with manual segmentation obtained by human experts. With our proposed method, the most daily-used estimation of cardiac function, e.g., ejection fraction, can be conducted in a much more efficient, accurate and convenient way.

Keywords

Random Forest Right Ventricle Direct Estimation Manual Segmentation Split Function 
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.
    Petitjean, C., Dacher, J.N.: A review of segmentation methods in short axis cardiac MR images. Medical Image Analysis 15(2), 169–184 (2011)CrossRefGoogle Scholar
  2. 2.
    Nambakhsh, C.M.S., Peters, T.M., Islam, A., Ben Ayed, I.: Right ventricle segmentation with probability product kernel constraints. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part I. LNCS, vol. 8149, pp. 509–517. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  3. 3.
    Afshin, M., Ayed, I.B., Islam, A., Goela, A., Peters, T.M., Li, S.: Global assessment of cardiac function using image statistics in MRI. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part II. LNCS, vol. 7511, pp. 535–543. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  4. 4.
    Wang, Z., Salah, M., Ayed, I., Islam, A., Goela, A., Li, S.: Bi-ventricular volume estimation for cardiac functional assessment. In: RSNA (2013)Google Scholar
  5. 5.
    Wang, Z., Ben Salah, M., Gu, B., Islam, A., Goela, A., Li, S.: Direct estimation of cardiac bi-ventricular volumes with an adapted bayesian formulation. IEEE TBME, 1251–1260 (2014)Google Scholar
  6. 6.
    Haber, I., Metaxas, D.N., Axel, L.: Three-dimensional motion reconstruction and analysis of the right ventricle using tagged MRI. Medical Image Analysis 4(4), 335–355 (2000)CrossRefGoogle Scholar
  7. 7.
    Lu, X., Wang, Y., Georgescu, B., Littman, A., Comaniciu, D.: Automatic delineation of left and right ventricles in cardiac MRI sequences using a joint ventricular model. In: Metaxas, D.N., Axel, L. (eds.) FIMH 2011. LNCS, vol. 6666, pp. 250–258. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Ben Ayed, I., Li, S., Ross, I.: Embedding overlap priors in variational left ventricle tracking. IEEE TMI 28(12), 1902–1913 (2009)zbMATHGoogle Scholar
  9. 9.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, vol. 1, pp. 886–893 (2005)Google Scholar
  10. 10.
    Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)CrossRefGoogle Scholar
  11. 11.
    Criminisi, A., Shotton, J.: Decision Forests for Computer Vision and Medical Image Analysis. Springer Publishing Company, Incorporated (2013)Google Scholar
  12. 12.
    Johnson, R., Zhang, T.: Learning nonlinear functions using regularized greedy forest. IEEE TPAMI 36, 942–954 (2014)CrossRefGoogle Scholar
  13. 13.
    Biau, G.: Analysis of a random forests model. JMLR 13, 1063–1095 (2012)zbMATHMathSciNetGoogle Scholar
  14. 14.
    Criminisi, A., Shotton, J., Robertson, D., Konukoglu, E.: Regression forests for efficient anatomy detection and localization in CT studies. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds.) MICCAI 2010. LNCS, vol. 6533, pp. 106–117. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Xiantong Zhen
    • 1
  • Zhijie Wang
    • 1
  • Ali Islam
    • 3
  • Mousumi Bhaduri
    • 4
  • Ian Chan
    • 4
  • Shuo Li
    • 1
    • 2
  1. 1.The University of Western OntarioLondonCanada
  2. 2.GE HealthcareLondonCanada
  3. 3.St. Joseph’s Health CareLondonCanada
  4. 4.London Health Sciences CentreCanada

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