Abstract
We describe a method for recovering the left intracardiac cavities from 3D Transesophageal Echocardiography (3D TEE). 3D TEE is an important modality for cardiac applications because of its ability to do fast and non-ionizing 3D imaging of the left heart complex. Segmentation based on 3D TEE can be used to characterize pathophysiologies of the valve and myocardium, and as input to patient-specific biomechanical models and preoperative planning tools. The segmentation employed here is based on a dynamic surface evolution. This is performed under a growth inhibition function that incorporates information from several sources including k-means clustering, 3D gradient magnitude, and a morphological structure tensor intended to locate the mitral valve leaflets. We report experiments using intraoperative 3D TEE data, showing good agreement between the segmented structures and ground truth.
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Burlina, P., Sprouse, C., DeMenthon, D., Jorstad, A., Juang, R., Contijoch, F., Abraham, T., Yuh, D., McVeigh, E.: Patient-specific modeling and analysis of the mitral valve using 3D-TEE. In: Information Processing in Computer-Assisted Interventions, pp. 135–146 (2010)
Sprouse, C., Yuh, D., Abraham, T., Burlina, P.: Computational hemodynamic modeling based on transesophageal echocardiographic imaging. In: Proc. Int. Conf. Engineering in Medicine and Biology Society, vol. 2009, pp. 3649–3652 (2009)
Mukherjee, R., Sprouse, C., Abraham, T., Hoffmann, B., McVeigh, E., Yuh, D., Burlina, P.: Myocardial motion computation in 4D ultrasound. In: Proc. Int. Symp. on Biomedical Imaging (2011)
Noble, J., Boukerroui, D.: Ultrasound image segmentation: A survey. IEEE Transactions on Medical Imaging 25(8), 987–1010 (2006)
Noble, J.: Ultrasound image segmentation and tissue characterization. Proc. of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 224(2), 307–316 (2010)
Hammoude, A.: Endocardial border identification in two-dimensional echocardiographic images: review of methods. Computerized Medical Imaging and Graphics 22(3), 181–193 (1998)
Mitchell, S., Lelieveldt, B., van der Geest, R., Bosch, H., Reiber, J., Sonka, M.: Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac MR images. IEEE Transactions on Medical Imaging 20, 415–423 (2001)
O’Brien, S., Ghita, O., Whelan, P.: Segmenting the left ventricle in 3D using a coupled ASM and a learned non-rigid spatial model. The MIDAS Journal 49 (August 2009)
Wijnhout, J., Hendriksen, D., Assen, H.V., der Geest, R.V.: LV challenge LKEB contribution: Fully automated myocardial contour detection. The MIDAS Journal 43 (August 2009)
Angelini, E., Homma, S., Pearson, G., Holmes, J., Laine, A.: Segmentation of real-time three-dimensional ultrasound for quantification of ventricular function: a clinical study on right and left ventricles. Ultrasound in Medicine & Biology 31(9), 1143–1158 (2005)
Duan, Q., Angelini, E., Laine, A.: Real-time segmentation by Active Geometric Functions. Computer Methods and Programs in Biomedicine 98(3), 223–230 (2010)
Qu, Y., Chen, Q., Heng, P., Wong, T.: Segmentation of left ventricle via level set method based on enriched speed term. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 435–442. Springer, Heidelberg (2004)
Chen, Y., Tagare, H., Thiruvenkadam, S., Huang, F., Wilson, D., Gopinath, K., Briggs, R., Geiser, E.: Using prior shapes in geometric active contours in a variational framework. International Journal of Computer Vision 50(3), 315–328 (2002)
Han, C., Lin, K., Wee, W., Mintz, R., Porembka, D.: Knowledge-based image analysis for automated boundary extraction of transesophageal echocardiographic left-ventricular images.. IEEE Transactions on Medical Imaging 10(4), 602 (1991)
Wolf, I., Hastenteufel, M., De Simone, R., Vetter, M., Glombitza, G., Mottl-Link, S., Vahl, C., Meinzer, H.: ROPES: A semiautomated segmentation method for accelerated analysis of three-dimensional echocardiographic data. IEEE Transactions on Medical Imaging 21(9), 1091–1104 (2003)
Kucera, D., Martin, R.: Segmentation of sequences of echocardiographic images using a simplified 3D active contour model with region-based external forces. Computerized Medical Imaging and Graphics 21(1), 1–21 (1997)
Cousty, J., Najman, L., Couprie, M., Clment-Guinaudeau, S., Goissen, T., Garot, J.: Segmentation of 4D cardiac MRI: automated method based on spatio-temporal watershed cuts. Image and Vision Computing 28(8), 1229–1243 (2010)
Ben Ayed, I., Punithakumar, K., Li, S., Islam, A., Chong, J.: Left ventricle segmentation via graph cut distribution matching. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5762, pp. 901–909. Springer, Heidelberg (2009)
Lu, Y., Radau, P., Connelly, K., Dick, A., Wright, G.: Automatic image-driven segmentation of left ventricle in cardiac cine MRI. The MIDAS Journal 49 (August 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)
Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Communications on Pure and Applied Mathematics 42(5), 577–685 (1989)
Malladi, R., Sethian, J., Vemuri, B.: Shape modeling with front propagation: A level set approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(2), 158–175 (2002)
Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. International Journal of Computer Vision 22(1), 61–79 (1997)
Li, C., Xu, C., Gui, C., Fox, M.D.: Level set evolution without re-initialization: a new variational formulation. In: Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, pp. 430–436. IEEE Computer Society, Los Alamitos (2005)
Sato, Y., Westin, C., Bhalerao, A., Nakajima, S., Shiraga, N., Tamura, S.: Tissue classification based on 3d local intensity structures for volume rendering. IEEE Transactions on Visualization and Computer Graphics (6) (2000)
Huang, A., Nielson, G., Razdan, A., Farin, G., Baluch, D., Capco, D.: Thin structure segmentation and visualization in three-dimensional biomedical images: a shape-based approach. IEEE Transactions on Visualization and Computer Graphics 12(1), 93–102 (2006)
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Burlina, P., Mukherjee, R., Juang, R., Sprouse, C. (2011). Recovering Endocardial Walls from 3D TEE. In: Metaxas, D.N., Axel, L. (eds) Functional Imaging and Modeling of the Heart. FIMH 2011. Lecture Notes in Computer Science, vol 6666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21028-0_35
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DOI: https://doi.org/10.1007/978-3-642-21028-0_35
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