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Segmentation of Abdominal Computed Tomography Scans Using Analysis of Texture Features and Its Application to Personalized Forward Electrocardiography Modeling

  • Alexander Danilov
  • Alexandra Yurova
Conference paper
Part of the Lecture Notes in Computational Science and Engineering book series (LNCSE, volume 131)

Abstract

Electrical activity of the heart muscles can be measured noninvasively on the body surface using electrocardiography (ECG) method. Numerical results of ECG modeling can be effectively used for diagnosis and treatment planning for cardiovascular diseases. In this paper we propose a method for personalized ECG modeling. One of the most important stages in this process is the generation of personalized torso voxel models, containing heart, lungs, fat, muscles and other organs of the abdominal cavity. Since abdomen segmentation is the most complicated and time consuming task, we propose an automated method for abdominal computed tomography scans (CT) segmentation, based on the texture analysis. We also present numerical analysis of the influence of some anatomical structures of the abdomen on the ECG modeling results.

Notes

Acknowledgements

The research was supported by the Russian Foundation for Basic Research (RFBR) under grants 17-01-00886 and 18-00-01524 (18-00-01661). The authors thank Sechenov University and Institute of Radiology (Rostock, Germany) for providing anonymized CT datasets.

References

  1. 1.
    Buades, A., Coll, B., Morel, J.-M.: Non-local means denoising. Image Process. Line 1, 208–212 (2011)zbMATHGoogle Scholar
  2. 2.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC-3, 610–621 (1973)CrossRefGoogle Scholar
  3. 3.
    Hofer, M.: CT Teaching Manual. Georg Thieme Verlag, Stuttgart (2007)Google Scholar
  4. 4.
    Keller, D.U., Weber, F.M., Seemann, G., Dössel, O.: Ranking the influence of tissue conductivities on forward-calculated ECGs. IEEE Trans. Biomed. Eng. 57, 1568–1576 (2010)CrossRefGoogle Scholar
  5. 5.
    Lines, G., Buist, M., Grottum, P., Pullan, A.J., Sundnes, J., Tveito, A.: Mathematical models and numerical methods for the forward problem in cardiac electrophysiology. Comput. Visual. Sci. 5, 215–239 (2003)CrossRefGoogle Scholar
  6. 6.
    Marquez-Neila, P., Baumela, L., Alvarez, L.: A morphological approach to curvature-based evolution of curves and surfaces. IEEE Trans. Pattern. Anal. Mach. Intell. 36, 2–17 (2014)CrossRefGoogle Scholar
  7. 7.
    Nielsen, B.F., Lysaker, M., Grøttum, P.: Computing ischemic regions in the heart with the bidomain model – first steps towards validation. IEEE Trans. Med. Imag. 32, 1085–96 (2013)CrossRefGoogle Scholar
  8. 8.
    Rineau, L., Yvinec M.: A generic software design for Delaunay refinement meshing. Comput. Geom. 38, 100–110 (2007)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Sethian, J.: Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science. Cambridge University Press, Cambridge (1999)zbMATHGoogle Scholar
  10. 10.
    Sundnes, J., Nielsen, B.F., Mardal, K.A., Cai, X., Lines, G.T., Tveito, A.: On the computational complexity of the bidomain and the monodomain models of electrophysiology. Ann. Biomed. Eng. 34, 1088–1097 (2006)CrossRefGoogle Scholar
  11. 11.
    Yushkevich, P.A., Piven, J., Hazlett, H.C., Smith, R.G., Ho, S., Gee, J.C., Gerig, G.: User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31, 1116–1128 (2006)CrossRefGoogle Scholar
  12. 12.
    Zemzemi, N., Bernabeu, M.O., Saiz, J., Cooper, J., Pathmanathan, P., Mirams, G.R., Pitt-Francis, J., Rodriguez, B.: Computational assessment of drug-induced effects on the electrocardiogram: from ion channel to body surface potentials. Br. J. Pharmacol. 168, 718–733 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alexander Danilov
    • 1
    • 2
    • 3
  • Alexandra Yurova
    • 3
  1. 1.Marchuk Institute of Numerical MathematicsRussian Academy of SciencesMoscowRussia
  2. 2.Moscow Institute of Physics and TechnologyDolgoprudnyRussia
  3. 3.Institute of Personalized MedicineSechenov UniversityMoscowRussia

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