Probabilistic Fitting of Active Shape Models

  • Andreas Morel-Forster
  • Thomas Gerig
  • Marcel Lüthi
  • Thomas Vetter
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11167)


Active Shape Models (ASMs) are a classical and widely used approach for fitting shape models to images. In this paper, we propose a fully probabilistic interpretation of ASM fitting as Bayesian inference. To infer the posterior, we use the Metropolis-Hastings algorithm. We then use the maximum a posteriori sample as the segmentation result. Our approach has several advantages compared to classical ASM fitting: (1) We are left with fewer parameters that we need to choose. (2) It is less prone to get trapped in local minima. (3) It becomes straightforward to extend the approach to include additional information, such as expert annotations. (4) It is even simpler to implement than the classical ASM fitting method.

We apply our algorithm to the SLIVER dataset and show that it achieves a higher segmentation accuracy than the standard ASM approach. We further demonstrate the flexibility and expressivity of the framework by integrating experts annotations along parts of the outline to further increase the accuracy. The code used for fitting is based on open-source software and made available to the community.


Active shape model Statistical shape model Gaussian process MCMC Sampling Metropolis Hastings Bayesian Liver 



This work was supported by the Innosuisse project 25622.1 PFLS-LS.


  1. 1.
    Cootes, T., Baldock, E., Graham, J.: An introduction to active shape models. In: Image Processing and Analysis, pp. 223–248 (2000)Google Scholar
  2. 2.
    Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models-their training and application. Comput. Vis. Image Underst. 61(1), 38–59 (1995)CrossRefGoogle Scholar
  3. 3.
    Esfandiarkhani, M., Foruzan, A.H.: A generalized active shape model for segmentation of liver in low-contrast CT volumes. Comput. Biol. Med. 82, 59–70 (2017)CrossRefGoogle Scholar
  4. 4.
    van Ginneken, B., de Bruijne, M., Loog, M., Viergever, M.A.: Interactive shape models. In: Medical Imaging 2003: Image Processing, vol. 5032, pp. 1206–1217. International Society for Optics and Photonics (2003)Google Scholar
  5. 5.
    Heimann, T., van Ginneken, B., et al.: Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans. Med. Imaging 28(8), 1251–1265 (2009). Scholar
  6. 6.
    Jampani, V., Nowozin, S., Loper, M., Gehler, P.V.: The informed sampler: a discriminative approach to Bayesian inference in generative computer vision models. Comput. Vis. Image Underst. 136, 32–44 (2015)CrossRefGoogle Scholar
  7. 7.
    Kirschner, M., Becker, M., Wesarg, S.: 3D active shape model segmentation with nonlinear shape priors. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6892, pp. 492–499. Springer, Heidelberg (2011). Scholar
  8. 8.
    Lindner, C., Thiagarajah, S., Wilkinson, J., Consortium, T., Wallis, G., Cootes, T.: Fully automatic segmentation of the proximal femur using random forest regression voting. IEEE Trans. Med. Imaging 32(8), 1462–1472 (2013)CrossRefGoogle Scholar
  9. 9.
    Lüthi, M., Gerig, T., Jud, C., Vetter, T.: Gaussian process morphable models. IEEE Trans. Pattern Anal. Mach. Intell. 40, 1860–1873 (2017)CrossRefGoogle Scholar
  10. 10.
    Norajitra, T., Maier-Hein, K.H.: 3D statistical shape models incorporating landmark-wise random regression forests for omni-directional landmark detection. IEEE Trans. Med. Imaging 36(1), 155–168 (2017)CrossRefGoogle Scholar
  11. 11.
    Schönborn, S., Egger, B., Morel-Forster, A., Vetter, T.: Markov chain Monte Carlo for automated face image analysis. Int. J. Comput. Vis. 123(2), 160–183 (2017). Scholar
  12. 12.
    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 Trans. Med. Imaging 21(8), 924–933 (2002)CrossRefGoogle Scholar
  13. 13.
    Wimmer, A., Soza, G., Hornegger, J.: A generic probabilistic active shape model for organ segmentation. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5762, pp. 26–33. Springer, Heidelberg (2009). Scholar
  14. 14.
    Zhang, Q., Bhalerao, A., Helm, E., Hutchinson, C.: Active shape model unleashed with multi-scale local appearance. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 4664–4668. IEEE (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Andreas Morel-Forster
    • 1
  • Thomas Gerig
    • 1
  • Marcel Lüthi
    • 1
  • Thomas Vetter
    • 1
  1. 1.University of BaselBaselSwitzerland

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