Abstract
Infarct size has been recognized as a good indicator of the functional status of the ischemic heart and to evaluate the impact of myocardial infarction therapies. Its assessment can be performed from late gadolinium enhancement magnetic resonance images. A number of methods have been proposed for the semi-automatic and automatic quantification of necrosis. We developed an automatic method based on a Markov random field framework and a region growing approach within an EM optimization, which enables segmentation of both necrosis and microvascular obstructions. The method has been evaluated on both synthetic data and 10 clinical cases in 3D and lead to the best results as compared to other conventional approaches and expertise.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arai, A.: The cardiac magnetic resonance approach to assessing myocardial viability. J. Nucl. Cardiol. 18(6), 1095–1102 (2011)
Wu, K.C.: CMR of microvascular obstruction and hemorrhage in myocardial infarction. J. Cardiovasc. Magn. Reson. 14, 68 (2012)
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. Eur. Radiol. 18(10), 2140–2147 (2008)
Positano, V., Pingitore, A., Giorgetti, A., Favilli, B., Santarelli, M.F., Landini, L., Marzullo, P., Lombardi, M.: A fast and effective method to assess myocardial necrosis by means of contrast magnetic resonance imaging. J. Cardiovasc. Magn. Reson. 7(2), 487–494 (2005)
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. J. Magn. Reson. Imaging 23(3), 298–308 (2006)
Hsu, L.-Y., Ingkanisorn, W.P., Kellman, P., Aletras, A.H., Arai, A.E.: Quantitative myocardial infarction on delayed enhancement MRI. Part II: clinical application of an automated feature analysis and combined thresholding infarct sizing algorithm. J. Magn. Reson. Imaging 23(3), 309–314 (2006)
Valindria, V. V., Angue, M., Vignon, N., Walker, P. M., Cochet, A., Lalande, A.: Automatic quantification of myocardial infarction from delayed enhancement MRI. In: 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems, pp. 277–283 (2011)
Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20(1), 45–57 (2001)
Barbosa, D., Dietenbeck, T., Schaerer, J., D’Hooge, J., Friboulet, D., Bernard, O.: B-spline explicit active surfaces: an efficient framework for real-time 3-D region-based segmentation. IEEE Trans. Image Process. 21(1), 241–251 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Viallon, M. et al. (2015). Automated Quantification of Myocardial Infarction Using a Hidden Markov Random Field Model and the EM Algorithm. In: van Assen, H., Bovendeerd, P., Delhaas, T. (eds) Functional Imaging and Modeling of the Heart. FIMH 2015. Lecture Notes in Computer Science(), vol 9126. Springer, Cham. https://doi.org/10.1007/978-3-319-20309-6_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-20309-6_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20308-9
Online ISBN: 978-3-319-20309-6
eBook Packages: Computer ScienceComputer Science (R0)