Abstract
At Siemens Corporate Research we have created a set of tools for the analysis of MR and CT cardiovascular images in the applications Argus, Vessel View, and Proteus. Argus is designed to assess cardiovascular function by reporting measures of morphology and tissue health using a 2-D approach. Vessel View, a 3-D application, is capable of quantifying vascular integrity and provides tools for segmenting vessels. Lastly, Proteus has functionality for registering 3-D cardiac data sets (e.g., MR and CT). Taken together, these applications allow for a comprehensive analysis of MR and CT cardiovascular studies. Throughout this paper we will illustrate the capabilities of our tools via their application to an actual clinical case. Our contribution lies in combining several computer vision technologies and applying them to practical, real world problems.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
AHA. 2001. Heart and Stroke Statistical Update. American Heart Association, Dallas, Tx.
Avants, B.B. and Williams, J.P. 2000. An adaptive minimal path generation technique for vessel tracking in CTA/CE-MRA volume images. In Medical Image Computing and Computer-Assisted Intervention, pp. 707–716.
Aylward, S., Pizer, S., Bullitt, E., and Eberly, D. 1996. Intensity ridge and widths for 3d object segmentation and description. In IEEE Proc. Workshop Mathematical Models Biomedical Image Analysis, pp. 131–138.
Bonneville, M. 1998. Support vector machines for improving the classification of brain PET images. In SPIE Medical Imaging 1998: Image Processing, vol. 3338, pp. 264–273.
Bullitt, E., Aylward, S., Liu, A., Stone, J., Mukherjee, S.K., Coey, C., Gerig, G., and Pizer, S.M. 1999. 3D graph description of the intracerebral vasculature from segmented MRA and tests of accuracy by comparison with X-ray angiograms. In Proc. Information Processing in Medical Imaging, pp. 308–321.
Chung, A. and Noble, J.A. 1999. Statistical 3D vessel segmentation using a Rician distribution. In Medical Image Conference and Computer Assisted Interventions, pp. 82–89.
Comaniciu, D. and Meer, P. 2002. Mean shift: A robust approach toward feature space analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence, 24(5).
Cristianini, N. 2000. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press.
Deschamps, T. and Cohen, L. 2001. Fast extraction of minimal paths in 3D images and applications to virtual endoscopy. Medical Image Analysis, 5(4):281–299.
Faber, T.L., Santana, C.A., Garcia, E.V., Candell-Riera, J., Folks, R.D., Peifer, J.W., Hopper, A., Aguade, S., Angel, J., and Klein, J.K. 2004. Fusion imaging: Combined visualization of 3D reconstructed coronary artery tree and 3D myocardial scinigraphic image in coronary artery disease. Int. J. Cardiac Imaging, 45(5):745–753.
Frangi, A., Rueckert, D., and Duncan, J.S. 2002. Three dimensional cardiovascular analysis. IEEE Trans. Medical Imaging, 21(9):1005–1010.
Frangi, A.F., Niessen, W.J., Vincken, K.L., and Viergever, M.A. 1998. Multiscale Vessel Enhancement Filtering. In Medical Image Conference and Computer Assisted Interventions, pp. 82–89.
Gokturk, S. 2001. A statistical 3-D pattern processing method for computer-aided detection of polyps in CT colonography. IEEE Trans. Medical Imaging, 20(12):1251–1260.
Gray, H. 1957. Gray’s Anatomy. Crown Publishers.
Jolly, M.-P. 2006. Automatic segmentation of the left ventricle in cardiac MR and CT images. International Journal of Computer Vision, (this issue).
Kim, R., Wu, E., and Rafael, A. 2000. The use of contrast-enhanced magnetic resonance imaging to identify reversible myocardial dysfunction. New England Journal of Medicine, 343:1445–1453.
Koller, T.M., Gerig, G., Szekely, G., and Dettwiler, D. 1995. Multiscale detection of curvilinear structures in 2-D and 3-D image data. In International Conference on Computer Vision, pp. 864–869.
Lorenz, C., Walker, E., Morgan, V., Klein, S., and Graham, T. 1999. Normal human right and left ventricle mass, systolic function, and gender differences by cine magnetic resonance imaging. Journal of Cardiovascular Magnetic Resonance, pp. 7–21.
Lorigo, L.M., Faugeras, O., Grimson, W.E.L., Keriven, R., Kikinis, R., Nabavi, A., and Westin, C. 2000. Codimension-two geodesic active contours for the segmentation of tubular structures. In IEEE Conference on Computer Vision and Pattern Recognition.
Metaxas, D. 1992. Physics-Based Modeling of Nonrigid Objects for Vision and Graphics. Ph.D. thesis, University of Toronto.
Murray, C. and Lopez, A. 2002. World Health Report: Reducing Risks, Promoting Healthy Life.
Nalwa, V. 1993. A Guided Tour of Computer Vision, Addison-Wesley.
Nekola, S., Ibrahim, T., and Balbach, T. 2001. Coregistration and Fusion of Cardiac Magnetic Resonance and Positron Emission Tomography Studies, vol. 322, pp. 144–154. IOS Press.
Noble, N., Hill, D., Breeuwer, M., and Razavi, R. 2004. The automatic identification of hibernating myocardium. In Medical Image Computing and Computer-Assisted Intervention, pp. 890–898.
O’Donnell, T., Aharon, S., Halliburton, S.S., Gupta, A., Funka-Lea, G., and White, R.D. 2000. Multi-modality model-based registration in the cardiac domain. In IEEE Conf. Computer Vision and Pattern Recognition, pp. 2790–2791.
O’Donnell, T. and Xu, N. 2003. Semi-automatic segmentation of non-viable cardiac tissue using cine and delayed enhancement magnetic resonance images. In SPIE Medical Imaging.
Pal, N.R. and Pal, S.K. 1993. A review on image segmentation techniques. Pattern Recognition, 26(9):1277–1294.
Petitjean, C., Rougon, N., and Cluzel, P. 2005. Assessment of myocardial function: A review of quantification methods and results using tagged MRI. Journal of Cardiovascular Magnetic Resonance, 7:501–516.
Rehr, R.B., Malloy, C.R., Filipchuk, N.G., and Peshock, R.M. 1985. Left Ventricular Volumes Measured by MR Imaging. Radiology, 156(3):717–719.
Sato, Y., Nakajima, S., Shiraga, N., Atsumi, H., Yoshida, S., Koller, T., Gerig, G., and Kikinis, R. 1998. Three-dimensional multi-scale line filter for segmentation and visualisation of curvilinear structures in medical images. Medical Image Analysis, 2(2):143–168.
Schiller, N.B., Shah, P.M., Crawford, M., DeMaria, A., Devereux, R., Feigenbaum, H., Gutgesell, H., Reichek, N., Sahn, D., and Schnittger, I. 1989. Recommendations for quantitation of the left ventricle by two-dimensional echocardiography, American Society of Echocardiography Committee on Standards, Subcommittee on Quantitation of Two-Dimensional Echocardiograms. J Am Soc Echocardiography, 2(5):358–367.
Schindler, T.H., Magosaki, N., Jeserich, M., Oser, U., Krause, T., Fischer, R., Moser, E., Nitzsche, E., Zehender, M., Just, H., and Solzbach, U. 1999. Fusion imaging: Combined visualization of 3D reconstructed coronary artery tree and 3D myocardial scinigraphic image in coronary artery disease. Int. J. Cardiac Imaging, 15:357–368.
Sethian, J.A. 1999. Level Set Methods and Fast Marching Methods. New York: Cambridge University Press, Second Ed.
Setser, R.M., O’Donnell, T.P., Smedira, N.G., Sabik, J.F., Halliburton, S.S., Stillman, A.E., and White, R.D. 2005. Co-registered MRI myocardial viability maps, MDCT coronary angiogram displays and surgical revascularization planning—initial experience. Radiology, 237(2): 465–473.
Sheehan, F.H., Braunwald, E., Canner, P., Dodge, H.T., Gore, J., Natta, P.V., Passamani, E.R., Williams, D.O., and Zaret, B. 1987. The effect of intravenous thrombolytic therapy on left ventricular function: A report on tissue-type plasminogen activator and streptokinase from the Thrombolysis in Myocardial Infarction (TIMI Phase I) trial. Circulation, 75(4):817–829.
Siddiqi, K. and Vasilevskiy, A. 2001. 3D flux maximizing flows. International Workshop on Energy Minimizing Methods In Computer Vision, pp. 336–650.
Sturm, B., Powell, K., Stillman, A., and White, R.D. 2003. Registration of 3D CT angiography and cardiac MR images in coronary artery disease patients. Int J. Cardiovascular Imaging, 19(4):281–293.
Tek, H., Comaniciu, D., and Williams, J. 2001. Vessel detection by mean shift based ray propagation. In Workshop on Mathematical Models in Biomedical Image Analysis, pp. 228–234.
Turkingson, T., DeGrad, M., Hanson, M., and Coleman, R. 1997. Alignment of dynamic cardiac PET images for correction of motion. IEEE Trans. Nuclear Science, 44:235–242.
Walimbe, V., Zagrodsky, V., Raja, S., Jaber, W.A., DiFilippo, F.P., Garcia, M.J., Brunken, R.C., Thomas, J.D., and Shekhar, R. 2003. Mutual information-based multimodality registration of cardiac ultrasound and SPECT images: A preliminary investigation. Int J. Cardiovascular Imaging, 19(6):483–494.
Wilson, D. and Noble, J. 1997. Segmentation of cerebral vessels and aneurysms from MR angiography data. In Proc. Information Processing in Medical Imaging, pp. 423–428.
Wirth, M., Choi, C., and Jennings, A. 1997. Point-to-point registration of nonrigid medical images using local elastic transformation methods. In IEEE Conf. on Image Processing and Its Applications, pp. 780–784.
Yezzi, A. and Prince, J.L. 2001. A PDE approach for measuring tissue thickness. In IEEE CVPR, pp. 87–92.
Yu, J.N., Fahey, F.H., Gage, H.D., Eades, C.G., Harkness, B.A., Pelizzari, C.A., and Jr., J.W.K. 1995. Intermodality retrospective image registration in the thorax. J. Nuclear Medicine, 36(12):2333–2338.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
O’Donnell, T., Funka-Lea, G., Tek, H. et al. Comprehensive Cardiovascular Image Analysis Using MR and CT at Siemens Corporate Research. Int J Comput Vision 70, 165–178 (2006). https://doi.org/10.1007/s11263-006-7937-2
Received:
Revised:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/s11263-006-7937-2