Learning Optical Flow Propagation Strategies Using Random Forests for Fast Segmentation in Dynamic 2D & 3D Echocardiography

  • Michael Verhoek
  • Mohammad Yaqub
  • John McManigle
  • J. Alison Noble
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7009)

Abstract

Fast segmentation of the left ventricular (LV) myocardium in 3D+time echocardiographic sequences can provide quantitative data of heart function that can aid in clinical diagnosis and disease assessment. We present an algorithm for automatic segmentation of the LV myocardium in 2D and 3D sequences which employs learning optical flow (OF) strategies. OF motion estimation is used to propagate single-frame segmentation results of the Random Forest classifier from one frame to the next. The best strategy for propagating between frames is learned on a per-frame basis. We demonstrate that our algorithm is fast and accurate. We also show that OF propagation increases the performance of the method with respect to the static baseline procedure, and that learning the best OF propagation strategy performs better than single-strategy OF propagation.

Keywords

Random Forest Optical Flow Left Ventricle Myocardium Candidate Test Target Frame 
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.
    Noble, J., Boukerroui, D.: Ultrasound image segmentation: a survey. IEEE Trans. Med. Imaging 25(8), 987–1010 (2006)CrossRefGoogle Scholar
  2. 2.
    Corsi, C., Saracino, G., et al.: Left ventricular volume estimation for real-time three-dimensional echocardiography. IEEE TMI 21(9), 1202–1208 (2002)Google Scholar
  3. 3.
    Angelini, E.D., Homma, S., Pearson, G., Holmes, J.W., Laine, A.F.: Segmentation of real-time three-dimensional ultrasound for quantification of ventricular function. Ultrasound Med. Biol. 31(9), 1143–1158 (2005)CrossRefGoogle Scholar
  4. 4.
    Leung, K.E., Bosch, J.G.: Automated border detection in three-dimensional echocardiography. Eur. J. Echocardiogr. 11(2), 97–108 (2010)CrossRefGoogle Scholar
  5. 5.
    Zhu, Y., Papademetris, X., Sinusas, A.J., Duncan, J.S.: A coupled deformable model for tracking myocardial borders from real-time echocardiography using an incompressibility constraint. Med. Image Anal. 14(3), 429–448 (2010)CrossRefGoogle Scholar
  6. 6.
    Myronenko, A., Song, X., Sahn, D.J.: LV motion tracking from 3D echocardiography using textural and structural information. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 428–435. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)CrossRefMATHGoogle Scholar
  8. 8.
    Amit, Y., Geman, D.: Shape quantization and recognition with randomized trees. Neural Comput. 9(7), 1545–1588 (1997)CrossRefGoogle Scholar
  9. 9.
    Andres, B., Köthe, U., Helmstaedter, M., Denk, W., Hamprecht, F.A.: Segmentation of SBFSEM volume data of neural tissue by hierarchical classification. In: Rigoll, G. (ed.) DAGM 2008. LNCS, vol. 5096, pp. 142–152. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Yi, Z., Criminisi, A., Shotton, J., Blake, A.: Discriminative, semantic segmentation of brain tissue in MR images. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5762, pp. 558–565. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Lempitsky, V., Verhoek, M., Noble, J.A., Blake, A.: Random forest classification for automatic delineation of myocardium in real-time 3D echocardiography. In: Ayache, N., Delingette, H., Sermesant, M. (eds.) FIMH 2009. LNCS, vol. 5528, pp. 447–456. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  12. 12.
    Geremia, E., Menze, B.H., Clatz, O., Konukoglu, E., Criminisi, A., Ayache, N.: Spatial decision forests for MS lesion segmentation in multi-channel MR images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6361, pp. 111–118. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)Google Scholar
  14. 14.
    Caruana, R., Niculescu-Mizil, A.: An empirical comparison of supervised learning algorithms. In: ICML 2006, pp. 161–168. ACM, New York (2006)Google Scholar
  15. 15.
    Bruhn, A., Weickert, J., Schnörr, C.: Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods. Int. J. Comp. Vision 61(3), 1–21 (2005)CrossRefGoogle Scholar
  16. 16.
    Shotton, J., Johnson, M., Cipolla, R.: Semantic texton forests for image categorization and segmentation. In: CVPR 2008, pp. 1–8 (2008)Google Scholar
  17. 17.
    Sharp, T.: Implementing decision trees and forests on a GPU. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 595–608. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  18. 18.
    Pauwels, K., Hulle, M.V.: Realtime phase-based optical flow on the GPU. In: CVPRW 2008, pp. 1–8 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Michael Verhoek
    • 1
  • Mohammad Yaqub
    • 1
  • John McManigle
    • 1
  • J. Alison Noble
    • 1
  1. 1.Institute of Biomedical EngineeringUniversity of OxfordUK

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