Automatic Detection of Cardiac Remodeling Using Global and Local Clinical Measures and Random Forest Classification

  • Jan EhrhardtEmail author
  • Matthias Wilms
  • Heinz Handels
  • Dennis Säring
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9534)


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.


Computer aided diagnosis Cardiac remodeling Myocardial infarction Random decision forests 



This work was supported by the German Research Foundation (DFG, EH 224/6-1).


  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3–42 (2006)CrossRefzbMATHGoogle Scholar
  8. 8.
    Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998)CrossRefGoogle Scholar
  9. 9.
    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)CrossRefGoogle Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    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)Google Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    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)CrossRefGoogle Scholar
  14. 14.
    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)Google Scholar
  15. 15.
    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)Google Scholar
  16. 16.
    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)CrossRefGoogle Scholar
  17. 17.
    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)CrossRefGoogle Scholar
  18. 18.
    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)CrossRefGoogle Scholar
  19. 19.
    Tolosi, L., Lengauer, T.: Classification with correlated features: unreliability of feature ranking and solutions. Bioinformatics 27(14), 1986–1994 (2011)CrossRefGoogle Scholar
  20. 20.
    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)Google Scholar
  21. 21.
    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)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jan Ehrhardt
    • 1
    Email author
  • Matthias Wilms
    • 1
  • Heinz Handels
    • 1
  • Dennis Säring
    • 2
    • 3
  1. 1.Institute of Medical InformaticsUniversity of LübeckLübeckGermany
  2. 2.Department of Computational NeuroscienceUniversity Medical Center Hamburg-EppendorfHamburgGermany
  3. 3.University of Applied SciencesWedelGermany

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