Myocardial Infarction Detection from Left Ventricular Shapes Using a Random Forest

  • Jack AllenEmail author
  • Ernesto Zacur
  • Erica Dall’Armellina
  • Pablo Lamata
  • Vicente Grau
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9534)


Understanding myocardial remodelling, and developing tools for its accurate quantification, is fundamental for improving the diagnosis and treatment of myocardial infarction patients. Conventional clinical metrics, such as blood pool volume or ejection fraction, are not always distinctive. Here we describe a method for the classification of myocardial infarction from 3D diastolic and systolic left ventricle shapes, represented by point sets. Classification features included global geometric, shape and thickness descriptors, and a random forest was used for classification. Results from cross validation show an accuracy of 92.5 % (leave-one-out) and 91.5 % (5-fold), improving the 87 % obtained with ejection fraction thresholds. These results suggest that refined remodelling metrics provide information beyond standard clinical descriptors.


Statistical shape model Random forest Left ventricle Myocardial infarction 



This work was supported by funding from the Medical Research Council (MRC) and Engineering and Physical Sciences Research Council (EPSRC) [grant number EP/L016052/1].


  1. 1.
    van Assen, H.C., Danilouchkine, M.G., Frangi, A.F., Ordas, S., Westenberg, J.J., Reiber, J.H., Lelieveldt, B.P.: SPASM: a 3D-ASM for segmentation of sparse and arbitrarily oriented cardiac MRI data. Med. Image Anal. 10(2), 286–303 (2006). CrossRefGoogle Scholar
  2. 2.
    Bild, D.E., Bluemke, D.A., Burke, G.L., Detrano, R., Diez Roux, A.V., Folsom, A.R., Greenland, P., Jacobs Jr., D.R., Kronmal, R., Liu, K., Nelson, J.C., OLeary, D., Saad, M.F., Shea, S., Szklo, M., Tracy, R.P.: Multi-ethnic study of atherosclerosis: objectives and design. Am. J. Epidemiol. 156(9), 871–881 (2002). CrossRefGoogle Scholar
  3. 3.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Cootes, T.F., Cooper, D.H., Taylor, C.J., Graham, J.: Trainable method of parametric shape description. Image Vis. Comput. 10(5), 289–294 (1992). CrossRefGoogle Scholar
  5. 5.
    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., MedranoGracia, 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
  6. 6.
    Kadish, A.H., Bello, D., Finn, P., Bonow, R.O., Schaechter, A., Subacius, H., Albert, C., Daubert, J.P., Fonseca, C., 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
  7. 7.
    Lewandowski, A.J., Augustine, D., Lamata, P., Davis, E.F., Lazdam, M., Francis, J., McCormick, K., Wilkinson, A., Singhal, A., Lucas, A., Smith, N., Neubauer, S., Leeson, P.: The preterm heart in adult life: cardiovascular magnetic resonance reveals distinct differences in left ventricular mass, geometry and function. Circulation 127(2), 197–206 (2012). CrossRefGoogle Scholar
  8. 8.
    Lorenz, C., Berg, J.V.: A comprehensive shape model of the heart. Med. Image Anal. 10(4), 657–670 (2006)CrossRefGoogle Scholar
  9. 9.
    Lötjönen, J., Kivistö, S., Koikkalainen, J., Smutek, D., Lauerma, K.: Statistical shape model of atria, ventricles and epicardium from short- and long-axis MR images. Med. Image Anal. 8(3), 371–386 (2004)CrossRefGoogle Scholar
  10. 10.
    Medrano-Gracia, P., Cowan, B.R., Bluemke, D.A., Finn, J.P., Kadish, A.H., Lee, D.C., Lima, J.A.C., Suinesiaputra, A., Young, A.A.: Atlas-based analysis of cardiac shape and function correction of regional shape bias due to imaging protocol for population studies. J. Cardiovasc. Magn. Reson (official journal of the Society for Cardiovascular Magnetic Resonance) 15, 80 (2013). ISBN:1097-6647CrossRefGoogle Scholar
  11. 11.
    Millán, R.D., Dempere-Marco, L., Pozo, J.M., Cebral, J.R., Frangi, F.: Morphological characterization of intracranial aneurysms using 3-D moment invariants. IEEE Trans. Med. Imaging 26(9), 1270–1282 (2007)CrossRefGoogle Scholar
  12. 12.
    Schroeder, W., Martin, K., Lorensen, B.: The Visualization Toolkit, 4th edn. Kitware Inc., Clifton Park (2006)Google Scholar
  13. 13.
    Sutton, M.G.S.J., Sharpe, N.: Left ventricular remodeling after myocardial infarction: pathophysiology and therapy. Circulation 101(25), 2981–2988 (2000). CrossRefGoogle Scholar
  14. 14.
    Wadell, H.: Sphericity and roundness of rock particles. J. Geol. 41(3), 310–331 (1933)CrossRefGoogle Scholar
  15. 15.
    Young, A.A., Frangi, A.F.: Computational cardiac atlases: from patient to population and back. Exp. Physiol. 94(5), 578–596 (2009). CrossRefGoogle Scholar
  16. 16.
    Young, A.A., Cowan, B.R., Thrupp, S.F., Hedley, W.J., DellItalia, L.J.: Left ventricular mass and volume: fast calculation with guide-point modeling on MR images. Radiology 216(2), 597–602 (2000). CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jack Allen
    • 1
    Email author
  • Ernesto Zacur
    • 2
  • Erica Dall’Armellina
    • 1
  • Pablo Lamata
    • 2
  • Vicente Grau
    • 1
  1. 1.University of OxfordOxfordUK
  2. 2.King’s College LondonLondonUK

Personalised recommendations