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Cardiac Medial Modeling and Time-Course Heart Wall Thickness Analysis

  • Hui Sun
  • Brian B. Avants
  • Alejandro F. Frangi
  • Federico Sukno
  • James C. Gee
  • Paul A. Yushkevich
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5242)

Abstract

The medial model is a powerful shape representation method that models a 3D object by explicitly defining its skeleton (medial axis) and deriving the boundary geometry according to medial geometry. It has been recently extended to model complex shapes with multi-figures, i.e., shapes whose skeletons can not be described by a single sheet in 3D. This paper applied the medial model to a 2-chamber heart data set consisting of 428 cardiac shapes from 90 subjects. The results show that the medial model can capture the heart shape accurately. To demonstrate the usage of the medial model, the changes of the heart wall thickness over time are analyzed. We calculated the mean heart wall thickness map of 90 subjects for different phases of the cardiac cycle, as well as the mean thickness change between phases.

Keywords

Medial Model Medial Axis Deformable Model Active Shape Model Binary Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Yushkevich, P.A., Zhang, H., Gee, J.: Continuous medial representation for anatomical structures. IEEE Trans. Med. Imaging 25(2), 1547–1564 (2006)CrossRefGoogle Scholar
  2. 2.
    Pizer, S.M., Fletcher, P.T., Joshi, S., Thall, A., Chen, J.Z., Fridman, Y., Fritsch, D.S., Gash, A.G., Glotzer, J.M., Jiroutek, M.R., Lu, C., Muller, K.E., Tracton, G., Yushkevich, P., Chaney, E.L.: Deformable m-reps for 3D medical image segmentation. International Journal of Computer Vision 55(2), 85–106 (2003)CrossRefGoogle Scholar
  3. 3.
    Azhari, H., Sideman, S., Weiss, J., Shapiro, E., Weisfeldt, M., Graves, W., Rogers, W., Beyar, R.: Three-dimensional mapping of acute ischemic regions using MRI: wall thickening versus motion analysis. Am. J. Physiol. 259, H1492–1503 (1990)Google Scholar
  4. 4.
    Sun, H., Avants, B.B., Frangi, A.F., Ordas, S., Gee, J.C., Yushkevich, P.A.: Branching medial models for cardiac shape representation. In: Proceedings of IEEE International Symposium on Biomedical Imaging (2008)Google Scholar
  5. 5.
    Bolson, E., Sheehan, F.: Centersurface model for 3d analysis of regional left ventricular function. In: Proceedings of Computers in Cardiology 1993, pp. 735–738 (September 1993)Google Scholar
  6. 6.
    Han, Q., Pizer, S.M., Merck, D., Joshi, S., Jeong, J.Y.: Multi-figure anatomical objects for shape statistics. In: Information Processing in Medical Imaging, pp. 701–712 (2005)Google Scholar
  7. 7.
    Terriberry, T.B.: Continuous Medial Models in Two-Sample Statistics of Shape. PhD thesis, University of North Carolina at Chapel Hill (2006)Google Scholar
  8. 8.
    Giblin, P., Kimia, B.: A formal classification of 3D medial axis points and their local geometry. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 566–573 (2000)Google Scholar
  9. 9.
    Joshi, S., Davis, B., Jomier, M., Gerig, G.: Unbiased diffeomorphic atlas construction for computational anatomy. Neuro. image 23 (Suppl. 1), 151–160 (2004)Google Scholar
  10. 10.
    Avants, B.B., Gee, J.C.: Symmetric geodesic shape averaging and shape interpolation. In: ECCV Workshops CVAMIA and MMBIA, pp. 99–110 (2004)Google Scholar
  11. 11.
    Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models – Their training and application. Computer Vision. Graphics and Image Processing 61(1), 38–59 (1995)Google Scholar
  12. 12.
    Assen, H.C.V., 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. Medical Image Analysis 10(2), 286–303Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Hui Sun
    • 1
  • Brian B. Avants
    • 1
  • Alejandro F. Frangi
    • 2
    • 3
  • Federico Sukno
    • 2
    • 3
  • James C. Gee
    • 1
  • Paul A. Yushkevich
    • 1
  1. 1.Departments of RadiologyUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Information and Communication Technologies DepartmentUniversity Pompeu FabraBarcelonaSpain
  3. 3.Networking Center on Biomedical ResearchCIBER-BBNBarcelonaSpain

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