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Fast and Robust Semi-automatic Liver Segmentation with Haptic Interaction

  • Erik Vidholm
  • Sven Nilsson
  • Ingela Nyström
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)

Abstract

We present a method for semi-automatic segmentation of the liver from CT scans. True 3D interaction with haptic feedback is used to facilitate initialization, i.e., seeding of a fast marching algorithm. Four users initialized 52 datasets and the mean interaction time was 40 seconds. The segmentation accuracy was verified by a radiologist. Volume measurements and segmentation precision show that the method has a high reproducibility.

Keywords

Segmentation Result Liver Volume Average Cost Seed Region Seed Point 
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.
    Olabarriaga, S.D., Smeulders, A.W.M.: Interaction in the segmentation of medical images: A survey. Medical Image Analysis 5(2), 127–142 (2001)CrossRefGoogle Scholar
  2. 2.
    Harders, M., Székely, G.: Enhancing human-computer interaction in medical segmentation. Proceedings of the IEEE 9(91), 1430–1442 (2003)CrossRefGoogle Scholar
  3. 3.
    Meinzer, H.P., Thorn, M., Cárdenas, C.E.: Computerized planning of liver surgery–an overview. Computers and Graphics 26, 569–576 (2002)CrossRefGoogle Scholar
  4. 4.
    Soler, L., Delingette, H., Malandain, G., et al.: Fully automatic anatomical, pathological, and functional segmentation from CT scans for hepatic surgery. Computer Aided Surgery 6(3), 131–142 (2001)CrossRefGoogle Scholar
  5. 5.
    Fauci, A.S., Braunwald, E., Isselbacher, K.J., Hauser, D.L., Kasper, D.L., Wilson, J.D., Martin, J.B., Longo, D.L. (eds.): Harrison’s Principles of Internal Medicine, 12th edn. McGraw-Hill, New York (1998)Google Scholar
  6. 6.
    Schenk, A., Prause, G., Peitgen, H.O.: Efficient semi-automatic segmentation of 3D objects in medical images. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 186–195. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  7. 7.
    Sethian, J.A.: Level set methods and fast marching methods. Cambridge University Press, Cambridge (1999)MATHGoogle Scholar
  8. 8.
    Kimmel, R., Sethian, J.A.: Optimal algorithm for shape from shading and path planning. Journal of Mathematical Imaging and Vision 14(3), 237–244 (2001)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proc. 6th Int. Conf. on Computer Vision, pp. 839–846 (1998)Google Scholar
  10. 10.
    Yan, J., Zhuang, T.: Applying improved fast marching method to echocardial boundary detection in echocardiographic images. Pattern Recognition Letters 24(15), 2777–2784 (2003)CrossRefGoogle Scholar
  11. 11.
    Thurfjell, L., McLaughlin, J., et al.: Haptic interaction with virtual objects: The technology and some applications. Industrial Robot 29(3), 210–215 (2002)CrossRefGoogle Scholar
  12. 12.
    Udupa, J.K., Leblanc, V.R., Schmidt, H., et al.: A methodology for evaluating image segmentation algorithms. In: Sonka, M., Fitzpatrick, J.M. (eds.) Proceedings of SPIE Medical Imaging 2002, SPIE, pp. 266–277 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Erik Vidholm
    • 1
  • Sven Nilsson
    • 2
  • Ingela Nyström
    • 1
  1. 1.Centre for Image AnalysisUppsala UniversitySweden
  2. 2.Dept. of Radiology and Clinical ImmunologyUppsala University HospitalSweden

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