Abstract
In this chapter, we present an automatic heart segmentation algorithm for the diagnosis of coronary artery diseases (CAD). The goal is to visualize the heart from a cardiac CT image with irrelevant tissues such as the lungs, rib cage, pulmonary veins, pulmonary arteries and left atrial appendage hidden so that doctors can clearly see the major coronary artery trees, aorta and bypass arteries if they exist. The algorithm combines a model-based detection framework with data-driven post-refinements to create a mask for a given cardiac CT image that contains only the relevant part of the heart. The marginal space learning [1] technique is used to localize mesh model or landmark points of different cardiovascular structures in the CT volume. Guided by such detected models, local data-driven voxel-based refinements are employed to produce precise boundaries of the heart mask. The algorithm is fully automatic and can process a 3D cardiac CT volume within a few seconds.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Four-chamber heart modeling and automatic segmentation for 3D cardiac CT volumes using marginal space learning and steerable features. IEEE Trans. Medical Imaging 27(11), 1668–1681 (2008)
Lloyd-Jones, D., Adams, R., Carnethon, M., et al.: Heart disease and stroke statistics. Circulation 119(3), 21–181 (2009)
Blaha, M., Budoff, M., DeFilippis, A., Blankstein, R., Rivera, J., Agatston, A., O’Leary, D., Lima, J., Blumenthal, R., Nasir, K.: Associations between C-reactive protein, coronary artery calcium, and cardiovascular events: implications for the JUPITER population from MESA, a population-based cohort study. The Lancet 378(9792), 684–692 (2011)
Funka-Lea, G., Boykov, Y., Florin, C., Jolly, M.P., Moreau-Gobard, R., Ramaraj, R., Rinck, D.: Automatic heart isolation for CT coronary visualization using graph-cuts. In: Proc. IEEE Int’l Sym. Biomedical Imaging, pp. 614–617 (2006)
Moreno, A., Takemura, C.M., Colliot, O., Camara, O., Bloch, I.: Using anatomical knowledge expressed as fuzzy constraints to segment the heart in CT images. Pattern Recognition 41(8), 2525–2540 (2008)
van Rikxoort, E.M., Isgum, I., Staring, M., Klein, S., van Ginneken, B.: Adaptive local multi-atlas segmentation: Application to heart segmentation in chest CT scans. In: Proc. of SPIE Medical Imaging (2008)
Lelieveldt, B.P.F., van der Geest, R.J., Rezaee, M.R., Bosch, J.G., Reiber, J.H.C.: Anatomical model matching with fuzzy implicit surfaces for segmentation of thoracic volume scans. IEEE Trans. Medical Imaging 18(3), 218–230 (1999)
Gregson, P.H.: Automatic segmentation of the heart in 3D MR images. In: Canadian Conf. Electrical and Computer Engineering, pp. 584–587 (1994)
Dryden, I.L., Mardia, K.V.: Statistical Shape Analysis. John Wiley, Chichester (1998)
Tu, Z.: Probabilistic boosting-tree: Learning discriminative methods for classification, recognition, and clustering. In: Proc. Int’l Conf. Computer Vision, pp. 1589–1596 (2005)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Sys., Man., Cyber. 9(1), 62–66 (1979)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models—their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)
Taubin, G.: Curve and surface smoothing without shrinkage. In: Proc. Int’l Conf. Computer Vision, pp. 852–857 (1995)
Zheng, Y., Loziczonek, M., Georgescu, B., Zhou, S.K., Vega-Higuera, F., Comaniciu., D.: Machine learning based vesselness measurement for coronary artery segmentation in cardiac CT volumes. In: Proc. of SPIE Medical Imaging, pp. 1–12 (2011)
Zheng, Y., John, M., Liao, R., Boese, J., Kirschstein, U., Georgescu, B., Zhou, S.K., Kempfert, J., Walther, T., Brockmann, G., Comaniciu, D.: Automatic aorta segmentation and valve landmark detection in C-arm CT: Application to aortic valve implantation. In: Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, pp. 1–8 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhong, H., Zheng, Y., Funka-Lea, G., Vega-Higuera, F. (2013). Automatic Heart Isolation in 3D CT Images. In: Menze, B.H., Langs, G., Lu, L., Montillo, A., Tu, Z., Criminisi, A. (eds) Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging. MCV 2012. Lecture Notes in Computer Science, vol 7766. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36620-8_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-36620-8_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36619-2
Online ISBN: 978-3-642-36620-8
eBook Packages: Computer ScienceComputer Science (R0)