Abstract
Myocardial infarction leads to a change in geometry and a modified motion characteristics of the heart, called remodeling. The detection of patients with subclinical remodeling is clinically relevant because effective therapies have to be initiated early to avoid a progressive dilatation, and deterioration in contractile function.
In this paper, we propose a classification approach to detect patients with cardiac remodeling based on established global and local clinical parameters, like end-diastolic and end-systolic volume, ejection fraction or local myocardial thickness. The functional parameters are extracted based on segmented endo- and epicardial contours using an in-house developed software tool. A random decision forest is trained for recognition of patients with impaired shape or motion characteristics. The 17 segment model of the left ventricle proposed by the American Heart Association is compared to a higher resolution model using 97 left ventricle segments in terms of classification performance.
The classification results are submitted to the left ventricle statistical shape modelling challenge with the aim to compare the classification performance of classical clinical parameters with other probabilistic or model-based approaches. A leave-one-out cross-validation shows an accuracy of 0.93 using global and local parameters compared to an accuracy of 0.86 using global parameters only.
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Bild, D.E., Bluemke, D.A., Burke, G.L., Detrano, R., Roux, A.V.D., Folsom, A.R., Greenland, P., Jacobs Jr., D.R., Kronmal, R., Liu, K., et al.: Multi-ethnic study of atherosclerosis: objectives and design. Am. J. Epidemiol. 156(9), 871–881 (2002)
Bosch, J.G., Nijland, F., Mitchell, S.C., Lelieveldt, B.P., Kamp, O., Reiber, J.H., Sonka, M.: Computer-aided diagnosis via model-based shape analysis: automated classification of wall motion abnormalities in echocardiograms. Acad. Radiol. 12(3), 358–367 (2005)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Cerqueira, M.D., Weissman, N.J., Dilsizian, V., Jacobs, A.K., Kaul, S., Laskey, W.K., Pennell, D.J., Rumberger, J.A., Ryan, T., Verani, M.S.: Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart: a statement for healthcare professionals from the cardiac imaging committee of the council on clinical cardiology of the american heart association. Circulation 105(4), 539–542 (2002)
Chykeyuk, K., Clifton, D., Noble, J.A., et al.: Feature extraction and wall motion classification of 2d stress echocardiography with relevance vector machines. In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 677–680. IEEE (2011)
Fonseca, C.G., Backhaus, M., Bluemke, D.A., Britten, R.D., Chung, J.D., Cowan, B.R., Dinov, I.D., Finn, J.P., Hunter, P.J., Kadish, A.H., Lee, D.C., Lima, J.A.C., Medrano-Gracia, P., Shivkumar, K., Suinesiaputra, A., Tao, W., Young, A.A.: The cardiac atlas project - an imaging database for computational modeling and statistical atlases of the heart. Bioinformatics 27(16), 2288–2295 (2011)
Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3–42 (2006)
Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998)
Kadish, A.H., Bello, D., Finn, J., Bonow, R.O., Schaechter, A., Subacius, H., Albert, C., Daubert, J.P., Fonseca, C.G., Goldberger, J.J.: Rationale and design for the defibrillators to reduce risk by magnetic resonance imaging evaluation (determine) trial. J. Cardiovasc. Electrophysiol. 20(9), 982–987 (2009)
Louppe, G., Wehenkel, L., Sutera, A., Geurts, P.: Understanding variable importances in forests of randomized trees. In: Advances in Neural Information Processing Systems, pp. 431–439 (2013)
Lund, G., Saering, D., Muellerleile, K., Cuerlis, J., Barz, D., Bannas, P., Radunski, U.K., Sydow, K., Adam, G.: Evaluation of a new semi-automatic strategy for quantitative measurement of infarct size in patients with acute and chronic myocardial infarction using cardiac magnetic resonance imaging. J. Cardiovasc. Magn. Reson. 15(1), P201 (2013)
Paetsch, I., Jahnke, C., Ferrari, V.A., Rademakers, F.E., Pellikka, P.A., Hundley, W.G., Poldermans, D., Bax, J.J., Wegscheider, K., Fleck, E., et al.: Determination of interobserver variability for identifying inducible left ventricular wall motion abnormalities during dobutamine stress magnetic resonance imaging. Eur. Heart J. 27(12), 1459–1464 (2006)
Punithakumar, K., Ben Ayed, I., Ross, I.G., Islam, A., Chong, J., Li, S.: Detection of left ventricular motion abnormality via information measures and bayesian filtering. IEEE Trans. Inf. Technol. Biomed. 14(4), 1106–1113 (2010)
Qazi, M., Fung, G., Krishnan, S., Rosales, R., Steck, H., Rao, R.B., Poldermans, D., Chandrasekaran, D.: Automated heart wall motion abnormality detection from ultrasound images using bayesian networks. IJCAI 7, 519–525 (2007)
Säring, D., Ehrhardt, J., Stork, A., Bansmann, M., Lund, G., Handels, H.: Computer-assisted analysis of 4D cardiac MR image sequences after myocardial infarction. Methods Inf. Med. 45(4), 377–383 (2006)
Sheehan, F.H., Bolson, E.L., Dodge, H.T., Mathey, D.G., Schofer, J., Woo, H.: Advantages and applications of the centerline method for characterizing regional ventricular function. Circulation 74(2), 293–305 (1986)
Suinesiaputra, A., Frangi, A., Kaandorp, T., Lamb, H., Bax, J., Reiber, J., Lelieveldt, B.: Automated detection of regional wall motion abnormalities based on a statistical model applied to multislice short-axis cardiac mr images. IEEE Trans. Med. Imaging 28(4), 595–607 (2009)
Then, J., Raman, V., Patrick Then, H.H., Enn Ong, S.E.: Literature review and proposed framework on CAD: automated cardiac MR images segmentation and classification. In: Papasratorn, B., Charoenkitkarn, N., Lavangnananda, K., Chutimaskul, W., Vanijja, V. (eds.) IAIT 2012. CCIS, vol. 344, pp. 170–180. Springer, Heidelberg (2012)
Tolosi, L., Lengauer, T.: Classification with correlated features: unreliability of feature ranking and solutions. Bioinformatics 27(14), 1986–1994 (2011)
Tsai, D.Y., Sekiya, M., Lee, Y.: Computer-aided diagnosis in abdominal and cardiac radiology using neural networks. In: Proceedings of the IEEE International Conference on Neural Information Processing. Citeseer (2001)
Zhang, X., Cowan, B.R., Bluemke, D.A., Finn, J.P., Fonseca, C.G., Kadish, A.H., Lee, D.C., Lima, J.A., Suinesiaputra, A., Young, A.A., et al.: Atlas-based quantification of cardiac remodeling due to myocardial infarction. PLoS One 9(10), e110243 (2014)
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This work was supported by the German Research Foundation (DFG, EH 224/6-1).
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Ehrhardt, J., Wilms, M., Handels, H., Säring, D. (2016). Automatic Detection of Cardiac Remodeling Using Global and Local Clinical Measures and Random Forest Classification. 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 2015. Lecture Notes in Computer Science(), vol 9534. Springer, Cham. https://doi.org/10.1007/978-3-319-28712-6_22
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DOI: https://doi.org/10.1007/978-3-319-28712-6_22
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