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.
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References
Noble, J., Boukerroui, D.: Ultrasound image segmentation: a survey. IEEE Trans. Med. Imaging 25(8), 987–1010 (2006)
Corsi, C., Saracino, G., et al.: Left ventricular volume estimation for real-time three-dimensional echocardiography. IEEE TMI 21(9), 1202–1208 (2002)
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)
Leung, K.E., Bosch, J.G.: Automated border detection in three-dimensional echocardiography. Eur. J. Echocardiogr. 11(2), 97–108 (2010)
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)
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)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Amit, Y., Geman, D.: Shape quantization and recognition with randomized trees. Neural Comput. 9(7), 1545–1588 (1997)
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)
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)
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)
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)
Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)
Caruana, R., Niculescu-Mizil, A.: An empirical comparison of supervised learning algorithms. In: ICML 2006, pp. 161–168. ACM, New York (2006)
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)
Shotton, J., Johnson, M., Cipolla, R.: Semantic texton forests for image categorization and segmentation. In: CVPR 2008, pp. 1–8 (2008)
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)
Pauwels, K., Hulle, M.V.: Realtime phase-based optical flow on the GPU. In: CVPRW 2008, pp. 1–8 (2008)
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Verhoek, M., Yaqub, M., McManigle, J., Noble, J.A. (2011). Learning Optical Flow Propagation Strategies Using Random Forests for Fast Segmentation in Dynamic 2D & 3D Echocardiography. In: Suzuki, K., Wang, F., Shen, D., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2011. Lecture Notes in Computer Science, vol 7009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24319-6_10
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DOI: https://doi.org/10.1007/978-3-642-24319-6_10
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