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Segmentation of the Aortic Valve Apparatus in 3D Echocardiographic Images: Deformable Modeling of a Branching Medial Structure

  • Alison M. PouchEmail author
  • Sijie Tian
  • Manabu Takabe
  • Hongzhi Wang
  • Jiefu Yuan
  • Albert T. Cheung
  • Benjamin M. Jackson
  • Joseph H. GormanIII
  • Robert C. Gorman
  • Paul A. Yushkevich
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8896)

Abstract

3D echocardiographic (3DE) imaging is a useful tool for assessing the complex geometry of the aortic valve apparatus. Segmentation of this structure in 3DE images is a challenging task that benefits from shape-guided deformable modeling methods, which enable inter-subject statistical shape comparison. Prior work demonstrates the efficacy of using continuous medial representation (cm-rep) as a shape descriptor for valve leaflets. However, its application to the entire aortic valve apparatus is limited since the structure has a branching medial geometry that cannot be explicitly parameterized in the original cm-rep framework. In this work, we show that the aortic valve apparatus can be accurately segmented using a new branching medial modeling paradigm. The segmentation method achieves a mean boundary displacement of 0.6 ± 0.1 mm (approximately one voxel) relative to manual segmentation on 11 3DE images of normal open aortic valves. This study demonstrates a promising approach for quantitative 3DE analysis of aortic valve morphology.

Keywords

Medial axis representation Deformable modeling Aortic valve 3D echocardiography 

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References

  1. 1.
    Ionasec, R.I., Voigt, I., Georgescu, B., Wang, Y., Houle, H., Vega-Higuera, F., Navab, N., Comaniciu, D.: Patient-specific modeling and quantification of the aortic and mitral valves from 4-D cardiac CT and TEE. IEEE Trans. Med. Imaging 29, 1636–1651 (2010)CrossRefGoogle Scholar
  2. 2.
    Pouch, A.M., Wang, H., Takabe, M., Jackson, B.M., Gorman III, J.H., Gorman, R.C., Yushkevich, P.A., Sehgal, C.M.: Fully Automatic Segmentation of the Mitral Leaflets Using Multi-Atlas Label Fusion and Deformable Medial Modeling. Med. Image Anal. 18(1), 118–129 (2014)CrossRefGoogle Scholar
  3. 3.
    Pouch, A.M., Wang, H., Takabe, M., Jackson, B.M., Sehgal, C.M., Gorman III, J.H., Gorman, R.C., Yushkevich, P.A.: Automated segmentation and geometrical modeling of the tricuspid aortic valve in 3D echocardiographic images. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part I. LNCS, vol. 8149, pp. 485–492. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  4. 4.
    Yushkevich, P.A., Zhang, H., Gee, J.C.: Continuous Medial Representation for Anatomical Structures. IEEE Trans. Med. Imaging 25(12), 1547–1564 (2006)CrossRefGoogle Scholar
  5. 5.
    Rausch, M.K., Famaey, N., Shultz, T.O., Bothe, W., Miller, D.C., Kuhl, E.: Mechanics of the Mitral Valve: A Critical Review, An In Vivo Parameter Identification, and the Effect of Prestrain. Biomech Model Mechanobiol 12(5), 1053–1071 (2013)CrossRefGoogle Scholar
  6. 6.
    Pizer, S.M., Fritsch, D.S., Yushkevich, P.A., Johnson, V.E., Chaney, E.L.: Segmentation, Registration, and Measurement of Shape Variation via Image Object Shape. IEEE Trans. Med. Imaging 18(1), 851–865 (1999)CrossRefGoogle Scholar
  7. 7.
    Sun, H., Frangi, A.F., Wang, H., Sukno, F.M., Tobon-Gomez, C., Yushkevich, P.A.: Automatic cardiac MRI segmentation using a biventricular deformable medial model. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part I. LNCS, vol. 6361, pp. 468–475. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Blum, H.: A transformation for extracting new descriptors of shape. In: Wathen-Dunn, W. (ed.) Models for the Perception of Speech and Visual Form, pp. 362–380. MIT Press, Cambridge (1967)Google Scholar
  9. 9.
    Yushkevich, P.A., Zhang, H.G.: Deformable modeling using a 3D boundary representation with quadratic constraints on the branching structure of the blum skeleton. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 280–291. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  10. 10.
    Wang, H., Suh, J.W., Das, S., Pluta, J., Craige, C., Yushkevich, P.: Multi-Atlas Segmentation with Joint Label Fusion. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 611–623 (2013)CrossRefGoogle Scholar
  11. 11.
    Wächter, A., Biegler, L.T.: On the Implementation of an Interior-Point Filter Line-Search Algorithm for Large-Scale Nonlinear Programming. Mathematical Programming 106, 25–57 (2006)CrossRefMathSciNetzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alison M. Pouch
    • 1
    Email author
  • Sijie Tian
    • 2
  • Manabu Takabe
    • 1
  • Hongzhi Wang
    • 3
  • Jiefu Yuan
    • 1
  • Albert T. Cheung
    • 4
  • Benjamin M. Jackson
    • 1
    • 5
  • Joseph H. GormanIII
    • 1
    • 5
  • Robert C. Gorman
    • 1
    • 5
  • Paul A. Yushkevich
    • 3
  1. 1.Gorman Cardiovascular Research GroupUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Department of Computer and Information ScienceUniversity of PennsylvaniaPhiladelphiaUSA
  3. 3.IBM Almaden Research CenterSan JoseUSA
  4. 4.Department of Anesthesiology, Perioperative, and Pain MedicineStanford School of MedicineStanfordUSA
  5. 5.Department of SurgeryUniversity of PennsylvaniaPhiladelphiaUSA

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