Learning a Global Descriptor of Cardiac Motion from a Large Cohort of 1000+ Normal Subjects

  • Wenjia BaiEmail author
  • Devis Peressutti
  • Ozan Oktay
  • Wenzhe Shi
  • Declan P. O’Regan
  • Andrew P. King
  • Daniel Rueckert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9126)


Motion, together with shape, reflect important aspects of cardiac function. In this work, a new method is proposed for learning of a cardiac motion descriptor from a data-driven perspective. The resulting descriptor can characterise the global motion pattern of the left ventricle with a much lower dimension than the original motion data. It has demonstrated its predictive power on two exemplar classification tasks on a large cohort of 1093 normal subjects.


Independent Component Analysis Cardiac Motion Locally Linear Embedding Gender Classification Template Image 
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.


  1. 1.
    Mor-Avi, V., Lang, R.M., Badano, L.P., Belohlavek, M., et al.: Current and evolving echocardiographic techniques for the quantitative evaluation of cardiac mechanics. Eur. J. Echocardiogr. 12(3), 167–205 (2011)CrossRefGoogle Scholar
  2. 2.
    Lang, R.M., Bierig, M., Devereux, R.B., Flachskampf, F.A., et al.: Recommendations for chamber quantification. Eur. J. Echocardiogr. 7(2), 79–108 (2006)CrossRefGoogle Scholar
  3. 3.
    Garcia-Barnés, J., Gil, D., Badiella, L., Hernandez-Sabate, A., et al.: A normalized framework for the design of feature spaces assessing the left ventricular function. IEEE Trans. Med. Imaging 29(3), 733–745 (2010)CrossRefGoogle Scholar
  4. 4.
    Lu, Y., Radau, P., Connelly, K., Dick, A., Wright, G.: Pattern recognition of abnormal left ventricle wall motion in cardiac MR. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part II. LNCS, vol. 5762, pp. 750–758. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  5. 5.
    Afshin, M., Ben Ayed, I., Punithakumar, K., Law, M., et al.: Regional assessment of cardiac left ventricular myocardial function via MRI statistical features. IEEE Trans. Med. Imaging 33(2), 481–494 (2014)CrossRefGoogle Scholar
  6. 6.
    Duchateau, N., De Craene, M., Piella, G., Silva, E., et al.: A spatiotemporal statistical atlas of motion for the quantification of abnormal myocardial tissue velocities. Med. Image Anal. 15(3), 316–328 (2011)CrossRefGoogle Scholar
  7. 7.
    Remme, E., Young, A.A., Augenstein, K.F., Cowan, B., Hunter, P.J.: Extraction and quantification of left ventricular deformation modes. IEEE Trans. Biomed. Eng. 51(11), 1923–1931 (2004)CrossRefGoogle Scholar
  8. 8.
    Leung, K.Y.E., Bosch, J.G.: Local wall-motion classification in echocardiograms using shape models and orthomax rotations. In: Sachse, F.B., Seemann, G. (eds.) FIHM 2007. LNCS, vol. 4466, pp. 1–11. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  9. 9.
    Suinesiaputra, A., Frangi, A.F., Kaandorp, T., Lamb, H.J., et al.: Automated detection of regional wall motion abnormalities based on a statistical model applied to multislice short-axis cardiac MR images. IEEE Trans. Med. Imaging 28(4), 595–607 (2009)CrossRefGoogle Scholar
  10. 10.
    Bhatia, K.K., Rao, A., Price, A., Wolz, R., Hajnal, J., Rueckert, D.: Hierarchical manifold learning for regional image analysis. IEEE Trans. Med. Imaging 33(2), 444–461 (2014)CrossRefGoogle Scholar
  11. 11.
    Ye, D.H., Desjardins, B., Hamm, J., Litt, H., Pohl, K.: Regional manifold learning for disease classification. IEEE Trans. Med. Imaging 33(6), 1236–1247 (2014)CrossRefGoogle Scholar
  12. 12.
    Shi, W., Jantsch, M., Aljabar, P., Pizarro, L., Bai, W., et al.: Temporal sparse free-form deformations. Med. Image Anal. 17(7), 779–789 (2013)CrossRefGoogle Scholar
  13. 13.
    Bai, W., Shi, W., O’Regan, D.P., Tong, T., et al.: A probabilistic patch-based label fusion model for multi-atlas segmentation with registration refinement: application to cardiac MR images. IEEE Trans. Med. Imaging 32(7), 1302–1315 (2013)CrossRefGoogle Scholar
  14. 14.
    Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18(8), 712–721 (1999)CrossRefGoogle Scholar
  15. 15.
    Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Wenjia Bai
    • 1
    Email author
  • Devis Peressutti
    • 2
  • Ozan Oktay
    • 1
  • Wenzhe Shi
    • 1
  • Declan P. O’Regan
    • 3
  • Andrew P. King
    • 2
  • Daniel Rueckert
    • 1
  1. 1.Biomedical Image Analysis Group, Department of ComputingImperial College LondonLondonUK
  2. 2.Division of Imaging Sciences and Biomedical EngineeringKing’s College LondonLondonUK
  3. 3.MRC Clinical Sciences Centre, Hammersmith HospitalImperial College LondonLondonUK

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