Abstract
Cardiac computed tomography (CT) is the primary non-invasive imaging modality to diagnose coronary artery disease. Though various methods have been proposed for coronary artery segmentation, most rely on at least one user click to provide a seed point for initialization. Automatic detection of the coronary ostia (where coronaries originate from the aorta), including both the native coronary ostia and graft ostia of the bypass coronaries, can make the whole coronary exam workflow fully automatic, therefore increasing a physician’s throughput. Anatomical structures (native coronary ostia) and pathological structures (graft ostia) often require significantly different detection methods. The native coronary ostia are well constrained by the surrounding structures, therefore are detected as a global object. Detecting the graft ostia is far more difficult due to the large variation in graft position. A new searching strategy is proposed to efficiently guide the focus of analysis and, at the same time, reduce the false positive detections. Since the bypass coronaries are grafted on the ascending aorta surface, the ascending aorta is first segmented to constrain the search. The quantitative prior distribution of the graft ostia on the aorta surface is learned from a training set to significantly reduce the searching space further. Efficient local image features are extracted around each candidate point on the aorta surface to train a detector. The proposed method is computationally efficient, taking about 0.40 seconds to detect both native and graft ostia in a volume with around 512×512×200 voxels.
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
Lloyd-Jones, D., Adams, R., Carnethon, M., et al.: Heart disease and stroke statistics – 2009 update. Circulation 119(3), 21–181 (2009)
Metz, C., Schaap, M., van Walsum, T., van der Giessen, A., Weustink, A., Mollet, N., Krestin, G., Niessen, W.: 3D segmentation in the clinic: A grand challenge II — coronary artery tracking. In: Proc. MICCAI Workshop on 3D Segmentation in the Clinic: A Grand Challenge (2008)
Hennemuth, A., Boskamp, T., Fritz, D., Kühnel, C., Bock, S., Rinck, D., Scheuering, M., Peitgen, H.O.: One-click coronary tree segmentation in CT angiographic images. Computer Assisted Radiology and Surgery, 317–321 (2005)
Wang, C., Smedby, O.: An automatic seeding method for coronary artery segmentation and skeletonization in CTA. The Insight Journal (2008)
Tek, H., Gulsun, M.A., Laguitton, S., Grady, L., Lesage, D., Funka-Lea, G.: Automatic coronary tree modeling. The Insight Journal (2008)
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)
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: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6361, pp. 476–483. Springer, Heidelberg (2010)
Tu, Z.: Probabilistic boosting-tree: Learning discriminative methods for classification, recognition, and clustering. In: Proc. Int’l. Conf. Computer Vision, pp. 1589–1596 (2005)
Liu, G., Haralick, R.M.: Optimal matching problem in detection and recognition performance evaluation. Pattern Recognition 35(3), 2125–2139 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zheng, Y., Tek, H., Funka-Lea, G., Zhou, S.K., Vega-Higuera, F., Comaniciu, D. (2011). Efficient Detection of Native and Bypass Coronary Ostia in Cardiac CT Volumes: Anatomical vs. Pathological Structures. In: Fichtinger, G., Martel, A., Peters, T. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. MICCAI 2011. Lecture Notes in Computer Science, vol 6893. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23626-6_50
Download citation
DOI: https://doi.org/10.1007/978-3-642-23626-6_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23625-9
Online ISBN: 978-3-642-23626-6
eBook Packages: Computer ScienceComputer Science (R0)