Active Shape Models for a Fully Automated 3D Segmentation of the Liver – An Evaluation on Clinical Data

  • Tobias Heimann
  • Ivo Wolf
  • Hans-Peter Meinzer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)


This paper presents an evaluation of the performance of a three-dimensional Active Shape Model (ASM) to segment the liver in 48 clinical CT scans. The employed shape model is built from 32 samples using an optimization approach based on the minimum description length (MDL). Three different gray-value appearance models (plain intensity, gradient and normalized gradient profiles) are created to guide the search. The employed segmentation techniques are ASM search with 10 and 30 modes of variation and a deformable model coupled to a shape model with 10 modes of variation. To assess the segmentation performance, the obtained results are compared to manual segmentations with four different measures (overlap, average distance, RMS distance and ratio of deviations larger 5mm). The only appearance model delivering usable results is the normalized gradient profile. The deformable model search achieves the best results, followed by the ASM search with 30 modes. Overall, statistical shape modeling delivers very promising results for a fully automated segmentation of the liver.


Shape Model Appearance Model Minimum Description Length Deformable Model Active Shape Model 
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.


  1. 1.
    Meinzer, H.P., Thorn, M., Cardenas, C.E.: Computerized planning of liver surgery – an overview. Computers & Graphics 26, 569–576 (2002)CrossRefGoogle Scholar
  2. 2.
    Soler, L., Delingette, H., Malandain, G., Montagnat, J., et al.: Fully automatic anatomical, pathological, and functional segmentation from ct scans for hepatic surgery. In: Proc. SPIE Medical Imaging, pp. 246–255 (2000)Google Scholar
  3. 3.
    Montagnat, J., Delingette, H.: Volumetric medical images segmentation using shape constrained deformable models. In: CVRMed, pp. 13–22 (1997)Google Scholar
  4. 4.
    Park, H., Bland, P.H., Meyer, C.R.: Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE TMI 22, 483–492 (2003)Google Scholar
  5. 5.
    Lamecker, H., Lange, T., Seebass, M.: Segmentation of the liver using a 3D statistical shape model. Technical report, Zuse Institute, Berlin (2004)Google Scholar
  6. 6.
    Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models – their training and application. CVIU 61, 38–59 (1995)Google Scholar
  7. 7.
    Cootes, T.F., Taylor, C.J.: Statistical models of appearance for computer vision. Technical report, Wolfson Image Analysis Unit, University of Manchester (2001)Google Scholar
  8. 8.
    Heimann, T., Wolf, I., Williams, T.G., Meinzer, H.P.: 3D Active Shape Models Using Gradient Descent Optimization of Description Length. In: Christensen, G.E., Sonka, M. (eds.) IPMI 2005. LNCS, vol. 3565, pp. 566–577. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Heimann, T., Wolf, I., Meinzer, H.P.: Optimal landmark distributions for statistical shape model construction. In: Proc. SPIE Medical Imaging, pp. 518–528 (2006)Google Scholar
  10. 10.
    Weese, J., Kaus, M., Lorenz, C., Lobregt, S., et al.: Shape constrained deformable models for 3D medical image segmentation. In: Insana, M.F., Leahy, R.M. (eds.) IPMI 2001. LNCS, vol. 2082, pp. 380–387. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  11. 11.
    van Ginneken, B., Frangi, A.F., Staal, J.J., ter Haar Romeny, B.M., Viergever, M.A.: Active shape model segmentation with optimal features. IEEE TMI 21, 924–933 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tobias Heimann
    • 1
  • Ivo Wolf
    • 1
  • Hans-Peter Meinzer
    • 1
  1. 1.Medical and Biological InformaticsGerman Cancer Research CenterHeidelberg

Personalised recommendations