Vessel Wall Segmentation Using Implicit Models and Total Curvature Penalizers

  • Rodrigo Moreno
  • Chunliang Wang
  • Örjan Smedby
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7944)

Abstract

This paper proposes an automatic segmentation method of vessel walls that combines an implicit 3D model of the vessels and a total curvature penalizer in a level set evolution scheme. First, the lumen is segmented by alternating a model-guided level set evolution and a recalculation of the model itself. Second, the level set of the lumen is evolved with a term that aims at penalizing the total curvature and with a prior that forces the outer layer of the vessel towards the outside of the lumen. The model term is deactivated during this step. Finally, in a third step, the model term is reactivated in order to impose a smooth change of the radius along the vessel. Once the two segmentations have been computed, stenoses are detected and quantified at the thickest locations of the segmented vessel wall. Preliminary results show that the proposed method compares favorably with respect to the state-of-the-art both for synthetic and real CTA datasets.

Keywords

Compute Tomography Angiography Compute Tomography Coronary Angiography Total Curvature Implicit Model Vessel 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.
    Kirisli, H., Schaap, M., Metz, C., Dharampal, A., Meijboom, W., Papadopoulou, S., Dedic, A., Nieman, K., de Graaf, M., Meijs, M., Cramer, M., Broersen, A., Cetin, S., Eslami, A., Florez-Valencia, L., Lor, K., Matuszewski, B., Melki, I., Mohr, B., Oksuz, I., Shahzad, R., Wang, C., Kitslaar, P., Unal, G., Katouzian, A., Orkisz, M., Chen, C., Precioso, F., Najman, L., Masood, S., Unay, D., van Vliet, L., Moreno, R., Goldenberg, R., Vucini, E., Krestin, G., Niessen, W., van Walsum, T.: Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography. Medical Image Analysis (2012) (submitted)Google Scholar
  2. 2.
    Wang, C., Moreno, R., Smedby, Ö.: Vessel segmentation using implicit model-guided level sets. In: Proceedings of 3D Cardiovascular Imaging: a MICCAI Segmentation Challenge Workshop (2012)Google Scholar
  3. 3.
    Mohr, B., Masood, S., Plakas, C.: Accurate lumen segmentation and stenosis detection and quantification in coronary CTA. In: Proceedings of 3D Cardiovascular Imaging: a MICCAI Segmentation Challenge Workshop (2012)Google Scholar
  4. 4.
    Lorigo, L.M., Faugeras, O.D., Grimson, W.E., Keriven, R., Kikinis, R., Nabavi, A., Westin, C.F.: CURVES: curve evolution for vessel segmentation. Medical Image Analysis 5(3), 195–206 (2001)CrossRefGoogle Scholar
  5. 5.
    Schaap, M., et al.: Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms. Medical Image Analysis 13(5), 701–714 (2009)CrossRefGoogle Scholar
  6. 6.
    Caselles, V., Kimmel, R., Sapiro, G., Sbert, C.: Minimal surfaces based object segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(4), 394–398 (1997)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Polden, A.: Curves and surfaces of least total curvature and fourth-order flows. PhD thesis, Tübingen University (1996)Google Scholar
  8. 8.
    Coleman, B., Falk, R., Moakher, M.: Spacetime finite element methods for surface diffusion with applications to the theory of the stability of cylinders. SIAM Journal on Scientific Computing 17(6), 1434–1448 (1996)MathSciNetMATHCrossRefGoogle Scholar
  9. 9.
    Chopp, D.L., Sethian, J.A.: Motion by intrinsic Laplacian of curvature. Interfaces and Free Boundaries 1, 1–18 (1999)MathSciNetGoogle Scholar
  10. 10.
    Droske, M., Rumpf, M.: A level set formulation for Willmore flow. Interfaces and Free Boundaries 6, 361–378 (2004)MathSciNetMATHCrossRefGoogle Scholar
  11. 11.
    Droske, M., Bertozzi, A.: Higher-order feature-preserving geometric regularization. SIAM Journal on Imaging Sciences 3(1), 21–51 (2010)MathSciNetMATHCrossRefGoogle Scholar
  12. 12.
    Burger, M., Stöcker, C., Voigt, A.: Finite element-based level set methods for higher order flows. Journal of Scientific Computing 35(2-3), 77–98 (2008)MathSciNetMATHCrossRefGoogle Scholar
  13. 13.
    Caselles, V., Haro, G., Sapiro, G., Verdera, J.: On geometric variational models for inpainting surface holes. Computer Vision and Image Understanding 111(3), 351–373 (2008)CrossRefGoogle Scholar
  14. 14.
    Chan, T.F., Kang, S.H., Shen, J.: Euler’s elastica and curvature based inpaintings. SIAM Journal on Applied Mathematics 63, 564–592 (2002)MathSciNetMATHGoogle Scholar
  15. 15.
    El-Zehiry, N.Y., Grady, L.: Vessel segmentation using 3D elastica regularization. In: Proceedings of International Symposium on Biomedical Imaging (ISBI), pp. 1288–1291 (2012)Google Scholar
  16. 16.
    Tasdizen, T., Whitaker, R.T., Burchard, P., Osher, S.: Geometric surface smoothing via anisotropic diffusion of normals. In: Proceedings of IEEE Visualization, pp. 125–132 (2002)Google Scholar
  17. 17.
    Goldenberg, R., Eilot, D., Begelman, G., Walach, E., Ben-Ishai, E., Peled, N.: Computer-aided simple triage (CAST) for coronary CT angiography (CCTA). International Journal Computer Assisted Radiology and Surgery, 1–9 (2012)Google Scholar
  18. 18.
    Wang, C., Frimmel, H., Smedby, Ö.: Level-set based vessel segmentation accelerated with periodic monotonic speed function. Proceedings of SPIE-Medical Imaging: Image Processing, vol. 7962, pp. 79621M-1–79621M-7 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Rodrigo Moreno
    • 1
  • Chunliang Wang
    • 1
  • Örjan Smedby
    • 1
  1. 1.Center for Medical Imaging Science and Visualization (CMIV), Department of Medical and Health Sciences (IMH)Linköping UniversityLinköpingSweden

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