Abstract
Myocardial viability assessment is an important task in the diagnosis of coronary heart disease. The measurement of the delayed enhancement effect, the accumulation of contrast agent in defective tissue, has become the gold standard for detecting necrotic tissue with MRI. The purpose of the presented work was to provide a segmentation and quantification method for delayed enhancement MRI. To this end, a suitable mixture model for the myocardial intensity distribution is determined based on expectation maximization and the comparison of the fit accuracy. The subsequent watershed-based segmentation uses the intensity threshold information derived from this model. Preliminary results are derived from an analysis of datasets provided by the STACOM challenge organizers. The segmentation provided reasonable results in all datasets, but the method strongly depends on the underlying myocardium segmentation.
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
Gerber, B.L., Rousseau, M.F., Ahn, S.A., le Polain de Waroux, J.B., Pouleur, A.C., Phlips, T., Vancraeynest, D., Pasquet, A., Vanoverschelde, J.L.J.: Prognostic value of myocardial viability by delayed-enhanced magnetic resonance in patients with coronary artery disease and low ejection fraction: impact of revascularization therapy. J. Am. Coll. Cardiol. 59(9), 825–835 (2012)
Kim, R., Fieno, D., Parrish, T., Harris, K., Chen, E., Simonetti, O., Bundy, J., Finn, J., Klocke, F., Judd, R.: Relationship of MRI delayed contrast enhancement to irreversible injury, infarct age, and contractile function. Circulation 100(19), 1992–2002 (1999)
Kolipaka, A., Chatzimavroudis, G., White, R., O’Donnell, T., Setser, R.: Segmentation of non-viable myocardium in delayed enhancement magnetic resonance images. Int. J. Cardiovasc. Imaging 21(2-3), 303–311 (2005)
Positano, V., Pingitore, A., Giorgetti, A., Favilli, B., Santarelli, M., Landini, L., Marzullo, P., Lombardi, M.: A Fast and Effective Method to Assess Myocardial Necrosis by Means of Contrast Magnetic Resonance Imaging. Journal of Cardiovascular Magnetic Resonance 7(2), 487–494 (2005)
O’Donnell, T., Xu, N., Setser, R., White, R.: Semi-automatic segmentation of nonviable cardiac tissue using cine and delayed enhancement magnetic resonance images. In: Proceedings of SPIE, vol. 5031, p. 242 (2003)
Hsu, L., Natanzon, A., Kellman, P., Hirsch, G., Aletras, A., Arai, A.: Quantitative myocardial infarction on delayed enhancement MRI, part I: animal validation of an automated feature analysis and combined thresholding infarct sizing algorithm. J. Magn. Reson. Imaging 23, 298–308 (2006)
Choi, K., Kim, R., Gubernikoff, G., Vargas, J., Parker, M., Judd, R.: Transmural Extent of Acute Myocardial Infarction Predicts Long-Term Improvement in Contractile Function. Circulation 104(10), 1101–1107 (2001)
Tao, Q., Milles, J., Zeppenfeld, K., Lamb, H., Bax, J., Reiber, J., van der Geest, R.: Automated segmentation of myocardial scar in late enhancement MRI using combined intensity and spatial information. Magn. Reson. Med. 64(2), 586–594 (2010)
Elagouni, K., Ciofolo-Veit, C., Mory, B.: Automatic segmentation of pathological tissue in cardiac MRI. In: Proceedings of the IEEE International Symposium on Biomedical Imaging, pp. 472–475 (2010)
Saering, D., Ehrhardt, J., Stork, A., Bansmann, P., Lund, G., Handels, H.: Analysis of the Left Ventricle after Myocardial Infarction combining 4D Cine-MR and 3D DE-MR Image Sequences. In: Bildverarbeitung fuer die Medizin, pp. 56–60 (2006)
Dietrich, O., Raya, J., Reeder, S., Ingrisch, M., Reiser, M., Schoenberg, S.: Influence of multichannel combination, parallel imaging and other reconstruction techniques on MRI noise characteristics. Magn. Reson. Imaging 26(6), 754–762 (2008)
Abramowitz, M., Stegun, I.A.: Handbook of Mathematical Functions. Dover Publications (1965)
Hennemuth, A., Seeger, A., Friman, O., Miller, S., Klumpp, B., Oeltze, S., Peitgen, H.O.: A comprehensive approach to the analysis of contrast enhanced cardiac MR images. IEEE Trans. Med. Imaging 27(11), 1592–1610 (2008)
Friman, O., Hennemuth, A., Peitgen, H.O.: A Rician-Gaussian Mixture Model for Segmenting Delayed Enhancement MRI Images. In: ISMRM 2008, p. 1040 (2008)
Hennemuth, A., Friman, O., Huellebrand, M., Peitgen, H., Mahnken, A.: Semi-Automatic Quantification of Late Enhancement in CT and MRI Images. In: ISMRM 2012, p. 1251 (2012)
Hunold, P., Schlosser, T., Barkhausen, J.: Magnetic resonance cardiac perfusion imaging-a clinical perspective. Eur. Radiol. 16(8), 1779–1788 (2006)
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
Hennemuth, A., Friman, O., Huellebrand, M., Peitgen, HO. (2013). Mixture-Model-Based Segmentation of Myocardial Delayed Enhancement MRI. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. STACOM 2012. Lecture Notes in Computer Science, vol 7746. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36961-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-36961-2_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36960-5
Online ISBN: 978-3-642-36961-2
eBook Packages: Computer ScienceComputer Science (R0)