Abstract
This paper presents a novel method for segmenting the coronary lumen in CTA data. The method is based on graph cuts, with edge-weights depending on the intensity of the centerline, and robust kernel regression. A quantitative evaluation in 28 coronary arteries from 12 patients is performed by comparing the semi-automatic segmentations to manual annotations. This evaluation showed that the method was able to segment the coronary arteries with high accuracy, compared to manually annotated segmentations, which is reflected in a Dice coefficient of 0.85 and average symmetric surface distance of 0.22 mm.
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References
Rosamond, W., et al.: Heart disease and stroke statistics–2008 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation 117, e25–e146 (2008)
Leber, A.W., et al.: Accuracy of 64-slice computed tomography to classify and quantify plaque volumes in the proximal coronary system: a comparative study using intravascular ultrasound. Journal of the American College of Cardiology 47, 672–677 (2006)
Rollano-Hijarrubia, E., Stokking, R., van der Meer, F., Niessen, W.J.: Imaging of small high-density structures in CT; A phantom study. Academic Radiology 13, 893–908 (2006)
Boskamp, T., Rinck, D., Link, F., Kümmerlen, B., Stamm, G., Mildenberger, P.: New vessel analysis tool for morphometric quantification and visualization of vessels in CT and MR imaging data sets. Radiographics 24(1), 287–297 (2004)
Luengo-Oroz, M.A., Ledesma-Carbayo, M.J., Gómez-Diego, J.J., García-Fernández, M.A., Desco, M., Santos, A.: Extraction of the Coronary Artery Tree in Cardiac Computer Tomographic Images Using Morphological Operators. In: Sachse, F.B., Seemann, G. (eds.) FIMH 2007. LNCS, vol. 4466, pp. 424–432. Springer, Heidelberg (2007)
Bouraoui, B., Ronse, C., Baruthio, J., Passat, N., Germain, P.: Fully automatic 3D segmentation of coronary arteries based on mathematical morphology. In: Proceedings of ISBI 2008, pp. 1059–1062 (2008)
Lesage, D., Angelini, E., Bloch, I., Funka-Lea, G.: Medial-based Bayesian tracking for vascular segmentation: Application to coronary arteries in 3D CT angiography. In: Proceedings of ISBI 2008, pp. 268–271 (2008)
Li, H., Yezzi, A.: Vessels as 4-D Curves: Global Minimal 4-D Paths to Extract 3-D Tubular Surfaces and Centerlines. IEEE Transactions on Medical Imaging 26, 1213–1223 (2007)
Wesarg, S., Firle, E.: Segmentation of Vessels: The Corkscrew Algorithm. In: SPIE: Medical Imaging: Image Processing, vol. 9, p. 10 (2004)
Yang, Y., Tannenbaum, A., Giddens, D., Stillman, A.: Automatic segmentation of coronary arteries using bayesian driven implicit surfaces. In: Proceedings of ISBI 2007, pp. 189–192 (2007)
Nain, D., Yezzi, A., Turk, G.: Vessel Segmentation Using a Shape Driven Flow. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 51–59. Springer, Heidelberg (2004)
Renard, F., Yang, Y.: Image analysis for detection of coronary artery soft plaques in MDCT images. In: Proceedings of ISBI 2008, pp. 25–28 (2008)
Lavi, G., Lessick, J., Johnson, P., Khullar, D.: Single-seeded coronary artery tracking in CT angiography. In: IEEE Nuclear Science Symposium Conference Record (2004)
Sonka, M., Winniford, M.D., Collins, S.M.: Robust simultaneous detection of coronary borders in complex images. IEEE Trans. Med. Imaging 14(1), 151–161 (1995)
Marquering, H.A., Dijkstra, J., de Koning, P.J.H., Stoel, B.C., Reiber, J.H.C.: Towards quantitative analysis of coronary CTA. Int. J. Cardiovasc. Imaging 21, 73–84 (2005)
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: IJ - 2008 MICCAI Workshop - Grand Challenge Coronary Artery Tracking (2008)
Wesarg, S., Khan, M.F., Firle, E.A.: Localizing calcifications in cardiac CT data sets using a new vessel segmentation approach. J. Digit. Imaging 19, 249–257 (2006)
Khan, M.F., Wesarg, S., Gurung, J., Dogan, S., Maataoui, A., Brehmer, B., Herzog, C., Ackermann, H., Assmus, B., Vogl, T.J.: Facilitating coronary artery evaluation in MDCT using a 3D automatic vessel segmentation tool. European Radiology 16, 1789–1795 (2006)
Boykov, Y., Funka-Lea, G.: Graph cuts and efficient n-d image segmentation. International Journal of Computer Vision 70, 109–131 (2006)
Boykov, Y., Veksler, O., Zabih, R.: Markov random fields with efficient approximations. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 648–655 (1998)
Kohli, P., Torr, P.H.S.: Dynamic graph cuts for efficient inference in markov random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(12), 2079–2088 (2007)
Nadaraya, E.A.: On estimating regression. Theory of Probability and its Applications 10, 186–190 (1964)
Knutsson, H., Westin, C.-F.: Normalized and differential convolution: Methods for interpolation and filtering of incomplete and uncertain data. In: Proceedings of Computer Vision and Pattern Recognition 1993, pp. 515–523 (1993)
Debruyne, M., Hubert, M., Suykens, J.: Model selection in kernel based regression using the influence function. Journal of Machine Learning Research 9, 2377–2400 (2008)
Boykov, Y., Kolmogorov, V.: Computing geodesics and minimal surfaces via graph cuts. In: ICCV (2003)
Hong, M.-K., et al.: The site of plaque rupture in native coronary arteries: a three-vessel intravascular ultrasound analysis. J. Am. Coll. Cardiol. 46, 261–265 (2005)
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Schaap, M. et al. (2009). Coronary Lumen Segmentation Using Graph Cuts and Robust Kernel Regression. In: Prince, J.L., Pham, D.L., Myers, K.J. (eds) Information Processing in Medical Imaging. IPMI 2009. Lecture Notes in Computer Science, vol 5636. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02498-6_44
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DOI: https://doi.org/10.1007/978-3-642-02498-6_44
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