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Automatic Hepatic Vessel Segmentation Using Graphics Hardware

  • Marius Erdt
  • Matthias Raspe
  • Michael Suehling
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5128)

Abstract

The accurate segmentation of liver vessels is an important prerequisite for creating oncologic surgery planning tools as well as medical visualization applications. In this paper, a fully automatic approach is presented to quickly enhance and extract the vascular system of the liver from CT datasets. Our framework consists of three basic modules: vessel enhancement on the graphics processing unit (GPU), automatic vessel segmentation in the enhanced images and an option to verify and refine the obtained results. Tests on 20 clinical datasets of varying contrast quality and acquisition phase were carried out to evaluate the robustness of the automatic segmentation. In addition the presented GPU based method was tested against a CPU implementation to demonstrate the performance gain of using modern graphics hardware. Automatic segmentation using graphics hardware allows reliable and fast extraction of the hepatic vascular system and therefore has the potential to save time for oncologic surgery planning.

Keywords

Segmentation Automation Computed Tomography Graphics Hardware Hepatic Vessels 

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References

  1. 1.
    Selle, D., Preim, B., Schenk, A., Peitgen, H.: Analysis of vasculature for liver surgical planning. IEEE Transactions on Medical Imaging 21, 1344–1357 (2002)CrossRefGoogle Scholar
  2. 2.
    Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496. Springer, Heidelberg (1998)Google Scholar
  3. 3.
    Sato, Y., Nakajima, S., Atsumi, H., Koller, T., Gerig, G., Yoshida, S., Kikinis, R.: 3d multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. In: Troccaz, J., Mösges, R., Grimson, W.E.L. (eds.) CVRMed-MRCAS 1997. LNCS, vol. 1205, pp. 213–222. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  4. 4.
    Manniesing, R., Viergever, M.A., Niessen, W.J.: Vessel enhancing diffusion: A scale space representation of vessel structures. Medical Image Analysis 10, 815–825 (2006)CrossRefGoogle Scholar
  5. 5.
    Koehler, H., Couprie, M., Bouattour, S., Paulus, D.: Extraction and analysis of coronary tree from single x-ray angiographies. In: Galloway Jr., R.L. (ed.) Medical Imaging 2004: Visualization, Image-Guided Procedures, and Display. Proceedings of the SPIE, May 2004, vol. 5367, pp. 810–819 (2004)Google Scholar
  6. 6.
    Langs, G., Radeva, P., Rotger, D.: Explorative building of 3d vessel tree models. In: Digital Imaging in Media and Education. 28th annual workshop of the Austrian Association for Pattern Recognition (OAGM/AAPR) (2004)Google Scholar
  7. 7.
    Owens, J.D., Luebke, D., Govindaraju, N., Harris, M., Krueger, J., Lefohn, A.E., Purcell, T.J.: A Survey of General-Purpose Computation on Graphics Hardware. Computer Graphics Forum 26(1), 80–113 (2007)CrossRefGoogle Scholar
  8. 8.
    Langs, A., Biedermann, M.: Filtering Video Volumes Using the Graphics Hardware. In: Ersbøll, B.K., Pedersen, K.S. (eds.) SCIA 2007. LNCS, vol. 4522, pp. 878–887. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Zhou, C., Chan, H.-P., Hadjiiski, L.M., Patel, S., Cascade, P.N., Sahiner, B., Wei, J., Ge, J., Kazerooni, E.A.: Automatic pulmonary vessel segmentation in 3D computed tomographic pulmonary angiographic (CTPA) images. In: Reinhardt, J.M., Pluim, J.P.W. (eds.) Medical Imaging 2006: Image Processing., March 2006, vol. 6144, pp. 1524–1530 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Marius Erdt
    • 1
  • Matthias Raspe
    • 2
  • Michael Suehling
    • 3
  1. 1.Fraunhofer Institute for Computer GraphicsCognitive Computing & Medical ImagingDarmstadtGermany
  2. 2.Institute of Computational VisualisticsUniversity of Koblenz-LandauKoblenzGermany
  3. 3.Computed Tomography: Physics & ApplicationsSiemens Medical SolutionsForchheimGermany

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