Advertisement

Tracking the Aortic Lumen Geometry by Optimizing the 3D Orientation of Its Cross-sections

  • Luis AlvarezEmail author
  • Agustín Trujillo
  • Carmelo Cuenca
  • Esther González
  • Julio Esclarín
  • Luis Gomez
  • Luis Mazorra
  • Miguel Alemán-Flores
  • Pablo G. Tahoces
  • José M. Carreira
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10434)

Abstract

We propose a fast incremental technique to compute the 3D geometry of the aortic lumen from a seed point located inside it. Our approach is based on the optimization of the 3D orientation of the cross-sections of the aorta. The method uses a robust ellipse estimation algorithm and an energy-based optimization technique to automatically track the centerline and the cross sections. In order to perform the optimization, we consider the size and the eccentricity of the ellipse which best fit the contour of the aorta on each cross-sectional plane. The method works directly on the original image and does not require a prior segmentation of the aortic lumen. We present some preliminary results which show the accuracy of the method and its ability to cope with challenging real CT (computed tomography) images of aortic lumens with significant angulations due to severe elongations.

Keywords

Aorta Ellipse tracking Centerline Cross-section CT 

Notes

Acknowledgement

This research has partially been supported by the MINECO projects references TIN2016-76373-P (AEI/FEDER, UE) and MTM2016-75339-P (AEI/FEDER, UE) (Ministerio de Economía y Competitividad, Spain).

Supplementary material

451304_1_En_20_MOESM1_ESM.mp4 (10.4 mb)
Supplementary material 1 (mp4 10678 KB)
451304_1_En_20_MOESM2_ESM.mp4 (6 mb)
Supplementary material 2 (mp4 6098 KB)
451304_1_En_20_MOESM3_ESM.mp4 (2 mb)
Supplementary material 3 (mp4 2012 KB)
451304_1_En_20_MOESM4_ESM.mp4 (3 mb)
Supplementary material 4 (mp4 3098 KB)

References

  1. 1.
    Alvarez, L., Trujillo, A., Cuenca, C., González, E., Gomez, L., Mazorra, L., Alemán-Flores, M., Tahoces, G., Pablo, C., José, M.: Ellipse tracking using active contour models (2017). Preprint http://www.ctim.es/papers/2017EllipseTrackingPreprint.pdf
  2. 2.
    Boskamp, T., Rinck, D., Link, F., Kummerlen, B., Stamm, G., Mildenberger, P.: New vessel analysis tool for morphometric quantification and visualization of vessels in CT and MR imaging data sets. RadioGraphics 24(1), 287–297 (2004)CrossRefGoogle Scholar
  3. 3.
    Brox, T., Kim, Y.J., Weickert, J., Feiden, W.: Fully-automated analysis of muscle fiber images with combined region and edge-based active contours. In: Handels, H., Ehrhardt, J., Horsch, A., Meinzer, H.P., Tolxdorff, T. (eds.) Bildverarbeitung frdie Medizin 2006. Informatik aktuell, pp. 86–90. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Delgado-Gonzalo, R., Uhlmann, V., Schmitter, D., Unser, M.: Snakes on a plane: a perfect snap for bioimage analysis. IEEE Sig. Process. Mag. 32(1), 41–48 (2015)CrossRefGoogle Scholar
  5. 5.
    Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A., Delp, S. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998). doi: 10.1007/BFb0056195 CrossRefGoogle Scholar
  6. 6.
    Hoyos, M.H., Orlowski, P., Pitkowska-Janko, E., Bogorodzki, P., Orkisz, M.: Vascular centerline extraction in 3D MR angiograms to optimize acquisition plane for blood flow measurement by phase contrast MRI. In: International Congress Series, vol. 1281, pp. 345–350 (2005)Google Scholar
  7. 7.
    Jacob, M., Blu, T., Unser, M.: Efficient energies and algorithms for parametric snakes. IEEE Trans. Image Process. 13, 1231–1244 (2004)CrossRefGoogle Scholar
  8. 8.
    Krissian, K., Malandain, G., Ayache, N., Vaillant, R., Trousset, Y.: Model-based detection of tubular structures in 3D images. Comput. Vis. Image Underst. 80(2), 130–171 (2000)CrossRefzbMATHGoogle Scholar
  9. 9.
    Lesage, D., Angelini, E., Bloch, I., Funka-Lea, G.: A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes. Med. Image Anal. 13(6), 819–845 (2009)CrossRefGoogle Scholar
  10. 10.
    Xie, Y., Padgett, J., Biancardi, A.M., Reeves, A.P.: Automated aorta segmentation in low-dose chest CT images. Int. J. Comput. Assist. Radiol. Surg. 9(2), 211–219 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Luis Alvarez
    • 1
    Email author
  • Agustín Trujillo
    • 1
  • Carmelo Cuenca
    • 1
  • Esther González
    • 1
  • Julio Esclarín
    • 1
  • Luis Gomez
    • 1
  • Luis Mazorra
    • 1
  • Miguel Alemán-Flores
    • 1
  • Pablo G. Tahoces
    • 2
  • José M. Carreira
    • 3
  1. 1.CTIM, DISUniversidad de Las Palmas de Gran CanariaLas Palmas de Gran CanariaSpain
  2. 2.CITIUSUniversidad de Santiago de CompostelaSantiago de CompostelaSpain
  3. 3.Complejo Hospitalario Universitario de Santiago (CHUS)Santiago de CompostelaSpain

Personalised recommendations