Advertisement

Systo-Diastolic LV Shape Analysis by Geometric Morphometrics and Parallel Transport Highly Discriminates Myocardial Infarction

  • Paolo Piras
  • Luciano TeresiEmail author
  • Stefano Gabriele
  • Antonietta Evangelista
  • Giuseppe Esposito
  • Valerio Varano
  • Concetta Torromeo
  • Paola Nardinocchi
  • Paolo Emilio Puddu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9534)

Abstract

We present a procedure that detects myocardial infarction by analyzing left ventricular shapes recorded at end-diastole and end-systole, involving both shape and statistical analyses. In the framework of Geometric Morphometrics, we use Generalized Procrustes Analysis, and optionally an Euclidean Parallel Transport, followed by Principal Components Analysis to analyze the shapes. We then test the performances of different classification methods on the dataset.

Among the different datasets and classification methods used, we found that the best classification performance is given by the following workflow: full shape (epicardium+endocardium) analyzed in the Shape Space (i.e. by scaling shapes at unit size); successive Parallel Transport centered toward the Grand Mean, in order to detect pure deformations; final statistical analysis via Support Vector Machine with radial basis Gaussian function. Healthy individuals show both a stronger contraction and a shape difference in systole with respect to pathological subjects. Moreover, endocardium clearly presents a larger deformation when contrasted with epicardium. Eventually, the solution for the blind test dataset is given. When using Support Vector Machine for learning from the whole training dataset and for successively classifying the 200 blind test dataset, we obtained 96 subjects classified as normal and 104 classified as pathological. After the disclosure of the blind dataset this resulted in 95 % of total accurracy with sensitivity at 97 % and specificity at 93 %.

Keywords

Geometric morphometrics Statistical shape analysis 

Supplementary material

416431_1_En_13_MOESM1_ESM.pdf (39 kb)
Supplementary material 1 (pdf 39 KB)

References

  1. 1.
    Adams, D.C., Rohlf, F.J., Slice, D.E.: Geometric morphometrics: ten years of progress following the revolution. Ital. J. Zool. 71, 5–16 (2004)CrossRefGoogle Scholar
  2. 2.
    Dryden, I.L., Mardia, K.V.: Statistical Shape Analysis. Wiley, Chichester (1998)zbMATHGoogle Scholar
  3. 3.
    Zhang, X., Cowan, B.R., Bluemke, D.A., Finn, J.P., Fonseca, C.G., Kadish, A.H., Lee, D.C., Lima, J.A.C., Suinesiaputra, A., Young, A.A., Medrano-Gracia, P.: Atlas-based quantification of cardiac remodeling due to myocardial infarction. PLoS ONE 9(10), e110243 (2014)CrossRefGoogle Scholar
  4. 4.
    Piras, P., Evangelista, A., Gabriele, S., Nardinocchi, P., Teresi, L., Torromeo, C., Schiariti, M., Varano, V., Puddu, P.E.: 4D-analysis of left ventricular heart cycle using Procrustes motion analysis. Plos One 9, e86896 (2014)CrossRefGoogle Scholar
  5. 5.
    Madeo, A., Piras, P., Re, F., Gabriele, S., Nardinocchi, P., Teresi, L., Torromeo, C., Chialastri, C., Schiariti, M., Giura, G., Evangelista, A., Dominici, T., Varano, V., Zachara, E., Puddu, P.E.: A new 4D trajectory-based approach unveils abnormal LV revolution dynamics in hypertrophic cardiomyopathy. PloS One 10(4), e0122376 (2015)CrossRefGoogle Scholar
  6. 6.
    Varano, V., Gabriele, S., Teresi, L., Dryden, I., Puddu, P.E., Torromeo, C., Piras, P.: Comparing shape trajectories of biological soft tissues in the size-and-shape. BIOMAT 2014 Congress Book (in press, 2015)Google Scholar
  7. 7.
    Efron, B.: The efficiency of logistic regression compared to normal discriminant analysis. J. Am. Stat. Assoc. 70, 892–898 (1975)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Peter, C.A.: A comparison of regression trees, logistic regression, generalized additive models, and multivariate adaptive regression splines for predicting AMI mortality. Stat. Med. 26, 2937–2957 (2007)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Maroco, J., Silva, D., Rodrigues, A., Guerreiro, M., Santana, I., de Mendonça, A.: Data mining methods in the prediction of Dementia: a real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC Res. Notes 4, 299 (2011)CrossRefGoogle Scholar
  10. 10.
    Puddu, P.E., Menotti, A.: Artificial neural networks versus proportional hazards Cox models to predict 45-year all-cause mortality in the Italian Rural Areas of the Seven Countries Study. BMC Res. Methodol. 12, 100 (2011)CrossRefGoogle Scholar
  11. 11.
    Goss, E.P., Ramchandani, H.: Comparing classification accuracy of neural networks, binary logit regression and discriminant analysis for insolvency prediction of life insurers. J. Econ. Finan. 19, 1–18 (1995)CrossRefGoogle Scholar
  12. 12.
    Fonseca, C.G., Backhaus, M., Bluemke, D.A., Britten, R.D., Do Chung, J., 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
  13. 13.
    Bookstein, F.L.: Morphometric Tools for Landmark Data. Cambridge University Press, Cambridge (1991)zbMATHGoogle Scholar
  14. 14.
    Rohlf, F.J.: Relative warp analysis and an example of its application to mosquito wings. In: Marcus L.F., Bello E., García-Valdecasa A., (eds.) Contributions to morphometrics, Museu Nacionale de Ciencias Naturales, pp. 131–159 (1993)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Paolo Piras
    • 1
  • Luciano Teresi
    • 2
    Email author
  • Stefano Gabriele
    • 2
  • Antonietta Evangelista
    • 1
  • Giuseppe Esposito
    • 1
  • Valerio Varano
    • 2
  • Concetta Torromeo
    • 1
  • Paola Nardinocchi
    • 1
  • Paolo Emilio Puddu
    • 1
  1. 1.SapienzaUniversità di RomaRomaItaly
  2. 2.Roma Tre UniversityRomaItaly

Personalised recommendations