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.
Chapter PDF
Similar content being viewed by others
Keywords
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
Petitjean, C., Dacher, J.N.: A review of segmentation methods in short axis cardiac MR images. Medical Image Analysis 15(2), 169–184 (2011)
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)
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)
Wang, Z., Salah, M., Ayed, I., Islam, A., Goela, A., Li, S.: Bi-ventricular volume estimation for cardiac functional assessment. In: RSNA (2013)
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)
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)
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)
Ben Ayed, I., Li, S., Ross, I.: Embedding overlap priors in variational left ventricle tracking. IEEE TMI 28(12), 1902–1913 (2009)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, vol. 1, pp. 886–893 (2005)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Criminisi, A., Shotton, J.: Decision Forests for Computer Vision and Medical Image Analysis. Springer Publishing Company, Incorporated (2013)
Johnson, R., Zhang, T.: Learning nonlinear functions using regularized greedy forest. IEEE TPAMI 36, 942–954 (2014)
Biau, G.: Analysis of a random forests model. JMLR 13, 1063–1095 (2012)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhen, X., Wang, Z., Islam, A., Bhaduri, M., Chan, I., Li, S. (2014). Direct Estimation of Cardiac Bi-ventricular Volumes with Regression Forests. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8674. Springer, Cham. https://doi.org/10.1007/978-3-319-10470-6_73
Download citation
DOI: https://doi.org/10.1007/978-3-319-10470-6_73
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10469-0
Online ISBN: 978-3-319-10470-6
eBook Packages: Computer ScienceComputer Science (R0)