Abstract
Multi-plane, 2D TEE images constitute the clinical standard of care for assessment of left ventricle function, as well as for guiding various minimally invasive procedure that rely on intra-operative imaging for real-time visualization. We propose a framework that enables automatic, rapid and accurate endocardial left ventricle feature identification and blood-pool segmentation using a combination of image filtering, graph cut, non-rigid registration-based motion extraction, and 3D LV geometry reconstruction techniques applied to the TEE image series. We evaluate our proposed framework using several retrospective patient tri-plane TEE image sequences and demonstrate comparable results to those achieved by expert manual segmentation using clinical software.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Noble, J., Boukerroui, D.: Ultrasound image segmentation: a survey. IEEE Trans. Med. Imaging 25(8), 987–1010 (2006)
Mishra, A., Dutta, P., Ghosh, M.: A GA based approach for boundary detection of left ventricle with echocardiographic image sequences. Image Vis. Comput. 21(11), 967–976 (2003)
Mignotte, M., Meunier, J.: A multiscale optimization approach for the dynamic contour-based boundary detection issue. Comput. Med. Imaging Graph. 25(3), 265–275 (2001)
Bernard, O., Friboulet, D., Thevenaz, P., Unser, M.: Variational B-spline level-set: a linear filtering approach for fast deformable model evolution. IEEE Trans. Image Process. 18(6), 1179–1191 (2009)
Cremers, D., Osher, S.J., Soatto, S.: Kernel density estimation and intrinsic alignment for shape priors in level set segmentation. Int. J. Comput. Vis. 69(3), 335–351 (2006)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models-their training and application. Comput. Vis. Image Underst. 61(1), 38–59 (1995)
Bosch, J., Mitchell, S., Lelieveldt, B., Nijland, F., Kamp, O., Sonka, M., Reiber, J.: Automatic segmentation of echocardiographic sequences by active appearance motion models. IEEE Trans. Med. Imaging 21(11), 1374–1383 (2002)
Georgescu, B., Zhou, X., Comaniciu, D., Gupta, A.: Database-guided segmentation of anatomical structures with complex appearance. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 429–436 (2005)
Jyh Herng Wu, E., De Andrade, M.L., Nicolosi, D.E., Pontes Jr., S.C.: Artificial neural network: border detection in echocardiography. Med. Biol. Eng. Comput. 46(9), 841–848 (2008)
Mulet-Parada, M., Noble, J.A.: 2D + T acoustic boundary detection in echocardiography. Med. Image Anal. 4, 21–30 (2000)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. PAMI 23, 1222–1239 (2001)
Kovesi, P.: Symmetry and asymmetry from local phase. In: Proceedings of 10th Australian Joint Conference Artificial Intelligence, pp. 2–4 (1997)
Rajpoot, K., Grau, V., Noble, J.A.: Local-phase based 3D boundary detection using monogenic signal and its application to real-time 3-D echocardiography images. In: Proceedings of IEEE International Symposium Biomedical Imaging, pp. 783–786 (2009)
Uzkent, B., Hoffman, M.J., Cherry, E., Cahill, N.: Processing IEEE western NY image signal process workshop, pp. 47–51 (2014)
Lamash, Y., Fischer, A., Carasso, S., Lessick, J.: Strain analysis from 4D cardiac CT image data. IEEE Trans. Biomed. Eng. 62, 511–521 (2015)
Zitova, B., Flusser, J.: Image registration methods: a survey. Image Vis. Comput. 21, 977–1000 (2003)
Lee, E.T.Y.: Choosing nodes in parametric curve interpolation. Comput. Aided Des. 21, 363–370 (1989)
Barber, C.B., Dobkin, D.P., Huhdanpaa, H.: The quickhull algorithm for convex hulls. ACM Trans. Math. Softw. 22, 469–483 (1996)
Acknowledgments
The authors would like to acknowledge Dr. Nathan Cahill for sharing his technical expertise and Aditya Daryanani for his help with image segmentation. In addition, we acknowledge funding support from the Kate Gleason Research Fund and the RIT College of Engineering Faculty Development Grant.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Dangi, S., Ben-Zikri, Y.K., Lamash, Y., Schwarz, K.Q., Linte, C.A. (2015). Automatic LV Feature Detection and Blood-Pool Tracking from Multi-plane TEE Time Series. In: van Assen, H., Bovendeerd, P., Delhaas, T. (eds) Functional Imaging and Modeling of the Heart. FIMH 2015. Lecture Notes in Computer Science(), vol 9126. Springer, Cham. https://doi.org/10.1007/978-3-319-20309-6_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-20309-6_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20308-9
Online ISBN: 978-3-319-20309-6
eBook Packages: Computer ScienceComputer Science (R0)