Advertisement

Towards Automatic Assessment of the Mitral Valve Coaptation Zone from 4D Ultrasound

  • Sandy EngelhardtEmail author
  • Nils LichtenbergEmail author
  • Sameer Al-Maisary
  • Raffaele De Simone
  • Helmut Rauch
  • Jens Roggenbach
  • Stefan Müller
  • Matthias Karck
  • Hans-Peter Meinzer
  • Ivo Wolf
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9126)

Abstract

The coaptation zone is the part of the two mitral valve leaflets that collide during the cardiac cycle. It is an important parameter for the valve’s function and closing capability, but difficult to assess. In this work, we present an automatic approach for leaflet segmentation from 4D ultrasound images, which incorporates steps for coaptation zone modelling and allows determining the coaptation zone from the resulting leaflet surface. The method segments the leaflets over the whole cardiac cycle given a previously segmented annulus model. To provide a meaningful analysis of the coaptation line assessment, the mean error between ground truth model and segmented model has been computed for each leaflet separately. For the anterior leaflet, we achieved a mean error of \(1.16 \pm 0.38\) mm and \(1.24 \pm 0.37\) mm for the posterior leaflet, respectively.

Keywords

Mitral Valve Maximum Intensity Projection Posterior Leaflet Mitral Valve Leaflet Leaflet Surface 
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.

Notes

Acknowlegdements

This research received support from the German Research Foundation (DFG), grant ME 833/12-2, SI 1349/1-2 and by the Collaborative Research Centre SFB/TRR 125 Cognition Guided Surgery within project B01.

Supplementary material

Supplementary material (mp4 272 KB)

References

  1. 1.
    Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1124–1137 (2004)CrossRefGoogle Scholar
  2. 2.
    Chen, L., McCulloch, A.D., May-Newman, K.: Nonhomogeneous deformation in the anterior leaflet of the mitral valve. Ann. Biomed. Eng. 32(12), 1599–1606 (2004)CrossRefGoogle Scholar
  3. 3.
    Cobey, F.C., Swaminathan, M., Phillips-Bute, B., et al.: Quantitative assessment of mitral valve coaptation using three-dimensional transesophageal echocardiography. Ann. Thorac. Surg. 97(6), 1998–2004 (2014)CrossRefGoogle Scholar
  4. 4.
    Gogoladze, G., Dellis, S.L., Donnino, R., et al.: Analysis of the mitral coaptation zone in normal and functional regurgitant valves. Ann. Thorac. Surg. 89(4), 1158–1161 (2010)CrossRefGoogle Scholar
  5. 5.
    Graser, B., Wald, D., Al-Maisary, S., Grossgasteiger, M., de Simone, R., Meinzer, H.-P., Wolf, I.: Using a shape prior for robust modeling of the mitral annulus on 4D ultrasound data. Int. J. Comput. Assist. Radiol. Surg. 9(4), 635–644 (2013)Google Scholar
  6. 6.
    Ionasec, R.I., Voigt, I., Georgescu, B., et al.: Patient-specific modeling and quantification of the aortic and mitral valves from 4-D cardiac CT and TEE. IEEE Trans. Med. Imaging 29(9), 1636–1651 (2010)CrossRefGoogle Scholar
  7. 7.
    Nielsen, S.L., Nygaard, H., Mandrup, L., et al.: Mechanism of incomplete mitral leaflet coaptation - interaction of chordal restraint and changes in mitral leaflet coaptation geometry. J. Biomed. Eng. 124(5), 596–608 (2002)Google Scholar
  8. 8.
    Nolden, M., Zelzer, S., Seitel, A., Wald, D., et al.: The medical imaging interaction toolkit: challenges and advances: 10 years of open-source development. Int. J. Comput. Assist. Radiol. Surg. 8(4), 607–620 (2013)CrossRefGoogle Scholar
  9. 9.
    Pouch, A., Wang, H., Takabe, M., et al.: Fully automatic segmentation of the mitral leaflets in 3D transesophageal echocardiographic images using multi-atlas joint label fusion and deformable medial modeling. Med. Image Anal. 18(1), 118–129 (2014)CrossRefGoogle Scholar
  10. 10.
    Saito, K., Okura, H., Watanabe, N., Obase, K., et al.: Influence of chronic tethering of the mitral valve on mitral leaflet size and coaptation in functional mitral regurgitation. JACC: Cardiovasc. Imaging 5(4), 337–345 (2012)Google Scholar
  11. 11.
    Schneider, R.J., Burke, W.C., Marx, G.R., del Nido, P.J., Howe, R.D.: Modeling mitral valve leaflets from three-dimensional ultrasound. In: Metaxas, D.N., Axel, L. (eds.) FIMH 2011. LNCS, vol. 6666, pp. 215–222. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  12. 12.
    Schneider, R.J., Perrin, D.P., Vasilyev, N.V., et al.: Mitral annulus segmentation from 3D ultrasound using graph cuts. IEEE Trans. Med. Imaging 29(9), 1676–1687 (2010)CrossRefGoogle Scholar
  13. 13.
    Schneider, R.J., Tenenholtz, N.A., Perrin, D.P., Marx, G.R., del Nido, P.J., Howe, R.D.: Patient-specific mitral leaflet segmentation from 4D ultrasound. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 520–527. Springer, Heidelberg (2011) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sandy Engelhardt
    • 1
    Email author
  • Nils Lichtenberg
    • 1
    • 2
    Email author
  • Sameer Al-Maisary
    • 3
  • Raffaele De Simone
    • 3
  • Helmut Rauch
    • 4
  • Jens Roggenbach
    • 4
  • Stefan Müller
    • 2
  • Matthias Karck
    • 3
  • Hans-Peter Meinzer
    • 1
  • Ivo Wolf
    • 1
    • 5
  1. 1.Medical and Biological Informatics, DKFZHeidelbergGermany
  2. 2.Institute for Computational VisualisticsUniversity of Koblenz-LandauKoblenzGermany
  3. 3.Department of Cardiac SurgeryUniversity Hospital HeidelbergHeidelbergGermany
  4. 4.Department of AnesthesiologyUniversity Hospital HeidelbergHeidelbergGermany
  5. 5.University of Applied ScienceMannheimGermany

Personalised recommendations