Model-Based Segmentation and Fusion of 3D Computed Tomography and 3D Ultrasound of the Eye for Radiotherapy Planning

  • M. Bach CuadraEmail author
  • S. Gorthi
  • F. I. Karahanoglu
  • B. Paquier
  • A. Pica
  • H. P. Do
  • A. Balmer
  • F. Munier
  • J.-Ph. Thiran
Part of the Computational Methods in Applied Sciences book series (COMPUTMETHODS, volume 19)


Computed Tomography (CT) represents the standard imaging modality for tumor volume delineation for radiotherapy treatment planning of retinoblastoma despite some inherent limitations. CT scan is very useful in providing information on physical density for dose calculation and morphological volumetric information but presents a low sensitivity in assessing the tumor viability. On the other hand, 3D ultrasound (US) allows a highly accurate definition of the tumor volume thanks to its high spatial resolution but it is not currently integrated in the treatment planning but used only for diagnosis and follow-up. Our ultimate goal is an automatic segmentation of gross tumor volume (GTV) in the 3D US, the segmentation of the organs at risk (OAR) in the CT and the registration of both modalities. In this paper, we present some preliminary results in this direction. We present 3D active contour-based segmentation of the eye ball and the lens in CT images; the presented approach incorporates the prior knowledge of the anatomy by using a 3D geometrical eye model. The automated segmentation results are validated by comparing with manual segmentations. Then, we present two approaches for the fusion of 3D CT and US images: (i) landmark-based transformation, and (ii) object-based transformation that makes use of eye ball contour information on CT and US images.


Parametric Active Contours Model-based segmentation Multi-model image fusion Ultrasound imaging Computer tomography Eye imaging radiotherapy 



This work is supported by the Centre d ́Imagerie BioMédicale (CIBM) of the University of Lausanne (UNIL), the Swiss Federal Institute of Technology Lausanne (EPFL), the University of Geneva (UniGe), the Centre Hospitalier Universitaire Vaudois (CHUV), the Hôpitaux Universitaires de Genève (HUG) and the Leenaards and the Jeantet Foundations.


  1. 1.
    BachCuadra, M., Gorthi, S., Karahanoglu, F.I., Salvador, F., Pica, A., Do, H., Balmer, A., Munier, F., Thiran, J.P.: Model-based segmentation and image fusion of 3D computed tomography and 3D ultrasound of the eye for radiotherapy planning. In: Second ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing, pp. 53–58 (2009)Google Scholar
  2. 2.
    Bekes, G., Máté, E., Nyúl, L.G., Kuba, A., Fidrich, M.: Geometrical model-based segmentation of the organs of sight on CT images. Med. Phys. 35, 735–743 (2008)CrossRefGoogle Scholar
  3. 3.
    Bondiau, P.Y., Malandain, G.: Eye reconstruction and CT-retinography fusion for proton treatment planning of ocular diseases. In: CVRMed-MRCAS’97, LNCS, vol. 1205, pp. 705–714. Springer (1997)Google Scholar
  4. 4.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vis. 22(1), 61–79 (1997)zbMATHCrossRefGoogle Scholar
  5. 5.
    Cates, J.E., Whitaker, R.T., Jones, G.M.: Case study: an evaluation of user-assisted hierarchical watershed segmentation. Med. Image Anal. 9(6), 566–578 (2005)CrossRefGoogle Scholar
  6. 6.
    DHaese, P., Duay, V., Li, R., duBoisdAische, A., Merchant, T., Cmelak, A., Donnelly, E., Niermann, K., Macq, B., Dawant, B.: Automatic segmentation of brain structures for radiation therapy planning. In: Medical Imaging: Image Processing, ISCAS. SPIE (2003)Google Scholar
  7. 7.
    Dobler, B., Bendl, R.: Precise modelling of the eye for proton therapy of intra-ocular tumours. Phys. Med. Biol. 47(4), 593–613 (2002)CrossRefGoogle Scholar
  8. 8.
    Donaldson, S., Smith, L.: Retinoblastoma: biology, presentation, and current management. Oncology 3(10), 45–51 (1989)Google Scholar
  9. 9.
    Fenster, A., Downey, D., Cardinal, H.: Three-dimensional ultrasound imaging. Phys. Med. Biol. 46(5), 67–99 (2001)Google Scholar
  10. 10.
    Maintz, J., Viergever, M.A.: A survey of medical image registration. Med. Image Anal. 2(1), 1–36 (1998)CrossRefGoogle Scholar
  11. 11.
    Maleike, D., Nolden, M., Meinzer, H.P., Wolf, I.: Interactive segmentation framework of the medical imaging interaction toolkit. Comput. Meth. Programs Biomed. 96(1), 72–83 (2009)CrossRefGoogle Scholar
  12. 12.
    Munier, F., Verwey, J., Pica, A., Balmer, A., Zografos, L., Abouzeid, H., Timmerman, B., Goitein, G., Moeckli, R.: New developments in external beam radiotherapy for retinoblastoma: from lens to normal tissue-sparing techniques. Clin. Exp. Ophthalmol. 36(1), 78–89 (2008)CrossRefGoogle Scholar
  13. 13.
  14. 14.
    Ophthalmic Technologies Inc. (OTI), Canada.
  15. 15.
    Penney, G., Blackall, J., Hamady, M., Sabharwal, T., Adam, A., Hawkes, D.: Registration of freehand 3D ultrasound and magnetic resonance liver images. Med. Image Anal. 8, 81–91 (2004)CrossRefGoogle Scholar
  16. 16.
    Roche, A., Pennec, X.M.G.A.N.: Rigid registration of 3-D ultrasound with MR images: a new approach combining intensity and gradient information. IEEE Tran. Med. Imag. 20(10), 1038–1049 (2001)Google Scholar
  17. 17.
    Souza, A., Ruiz, E.: Fast and accurate detection of extraocular muscle borders using mathematical morphology. In: Engineering in Medicine and Biology Society, 2000. Proceedings of the 22nd Annual International Conference of the IEEE, vol.3, pp. 1779–1782 (2000)Google Scholar
  18. 18.
    Wein, W., Brunke, S., Khamene, A., Callstrom, M., Navab, N.: Automatic CT-ultrasound registration for diagnostic imaging and image-guided intervention. Med. Image Anal. 12, 577–585 (2008)CrossRefGoogle Scholar
  19. 19.
    Yannuzzi, L.A., Ober, M.D., Slakter, J.S., Spaide, R.F., Fisher, Y.L., Flower, R.W., Rosen, R.: Ophthalmic fundus imaging: today and beyond. Am. J. Ophthalmol. 137(3), 511–524 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • M. Bach Cuadra
    • 1
    Email author
  • S. Gorthi
    • 1
  • F. I. Karahanoglu
    • 1
  • B. Paquier
    • 1
  • A. Pica
    • 2
  • H. P. Do
    • 3
  • A. Balmer
    • 4
  • F. Munier
    • 4
  • J.-Ph. Thiran
    • 1
  1. 1.Signal Processing Laboratory (LTS5)Ecole Polytechnique Fédérale de LausanneLausanneSwitzerland
  2. 2.Radiation Oncology DepartmentLausanne University Hospital (CHUV)LausanneSwitzerland
  3. 3.Institute of Applied RadiophysicsLausanneSwitzerland
  4. 4.Ophthalmic Hospital Jules GoninLausanneSwitzerland

Personalised recommendations