Abstract
We propose an automatic technique to segment scar and classify the myocardial tissue of the left ventricle from Delay Enhancement (DE) MRI. The method uses multiple region growing with two types of regions and automatic seed initialization. The region growing criteria is based on intensity distance and the seed initialization is based on a thresholding technique. We refine the obtained segmentation with some morphological operators and geometrical constraints to further define the infarcted area. Thanks to the use of two types of regions when performing the region growing, we are able to segment and classify the healthy and pathological tissues. We have also a third type of tissue in our classification, which includes tissue areas that deserve special attention from medical experts: border-zone tissue or myocardial segmentation errors.
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References
Vogel-Claussen, J., Rochitte, C., Wu, K.C., Kamel, I.R., Foo, T.K., Lima, J.A.C., Bluemke, D.: Delayed enhancement MR imaging: Utility in myocardial assessment. RadioGraphics 26, 795–810 (2006)
Kim, R.J., Fieno, D.S., Parrish, T.B., Harris, K., Chen, E., Simonetti, O., Bundy, J., Finn, J.P., Klocke, F.J., Judd, R.M.: Relationship of MRI delayed contrast enhancement to irreversible injury, infarct age, and contractile function. Circulation 100, 1992–2002 (1999)
Dikici, E., O’Donnell, T., Setser, R., White, R.D.: Quantification of Delayed Enhancement MR Images. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 250–257. Springer, Heidelberg (2004)
Berbari, R.E., Kachenoura, N., Frouin, F., Herment, A., Mousseaux, E., Bloch, I.: An automated quantification of the transmural myocardial infarct extent using cardiac DE-MR images. In: Proc. Int. Conf. IEEE Engineering in Medicine and Biology Society (EMBS) (2009)
Albà, X., Figueras i Ventura, R., Lekadir, K., Frangi, A.F.: Conical deformable model for myocardial segmentation in late-enhanced MRI. In: IEEE International Symposium on Biomedical Imaging (ISBI) (2012)
Heiberg, E., Engblom, H., Engvall, J., Hedström, E., Ugander, M., Arheden, H.: Semi-automatic quantification of myocardial infarction from delayed contrast enhanced magnetic resonance imaging. Scandinavian Cardiovascular Journal 39, 267–275 (2005)
Breeuwer, M., Paetsch, I., Nagel, E., Muthupillai, R., Flamm, S., Plein, S., Ridgway, J.: The detection of normal, ischemic and infarcted myocardial tissue using MRI. Computer Assisted Radiology and Surgery 1256, 1153–1158 (2003)
Kolipaka, A., Chatzimavroudis, G.P., White, R.D., O’Donnell, T.P., Setser, R.M.: Segmentation of non-viable myocardium in delayed enhancement magnetic resonance images. International Journal of Cardiovascular Imaging 21, 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, 487–494 (2005)
O’Donnell, T.P., Xu, N., Setser, R.M., White, R.D.: Semi-automatic segmentation of nonviable cardiac tissue using cine and delayed enhancement magnetic resonance images. In: Proc. SPIE Medical Imaging, vol. 5031, pp. 242–251 (2003)
Hsu, L.Y., Natanzon, A., Kellman, P., Hirsch, G.A., Aletras, A.H., Arai, A.E.: Quantitative myocardial infarction on delayed enhancement MRI. Part I: animal validation of an automated feature analysis and combined thresholding infarct sizing algorithm. Journal Magnetic Resonance Imaging 23, 298–308 (2006)
Tao, Q., Milles, J., Zeppenfeld, K., Lamb, H.J., Bax, J.J., Reiber, J.H., van der Geest, R.J.: Automated segmentation of myocardial scar in late enhancement MRI using combined intensity and spatial information. Magnetic Resonance in Medicine 64, 586–594 (2010)
Kachenoura, N., Redheuil, A., Herment, A., Mousseaux, E., Frouin, F.: Robust assessment of the transmural extent of myocardial infarction in late gadolinium-enhanced MRI studies using appropriate angular and circumferential subdivision of the myocardium. European Radiology 18, 2140–2147 (2008)
Hennemuth, A., Seeger, A., Friman, O., Miller, S., Klumpp, B., Oeltze, S., Peitgen, H.: A comprehensive approach to the analysis of contrast enhanced cardiac MR images. IEEE Transactions on Medical Imaging 27, 1592–1610 (2008)
Elagouni, K., Ciofolo-Veit, C., Mory, B.: Automatic segmentation of pathological tissues in cardiac MRI. In: IEEE International Symposium on Biomedical Imaging (ISBI), pp. 472–475 (2010)
Elnakib, A., Beache, G.M., Nitzken, M., Gimel’jarb, G., EI-Baz, A.: A new framework for automated identification of pathological tissues in contrast enhanced cardiac magnetic resonance images. In: IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1272–1275 (2011)
Adams, R., Bischof, L.: Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(6), 641–647 (1994)
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Albà, X., Figueras i Ventura, R.M., Lekadir, K., Frangi, A.F. (2013). Healthy and Scar Myocardial Tissue Classification in DE-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_8
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DOI: https://doi.org/10.1007/978-3-642-36961-2_8
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