3D Automatic Segmentation of the Hippocampus Using Wavelets with Applications to Radiotherapy Planning

  • Yi Gao
  • Benjamin W. Corn
  • Dan Schifter
  • Allen Tannenbaum
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 83)


During the past half-century, the cornerstone of treatment for brain metastases has been whole brain irradiation (WBI). WBI has multiple salutary effects including rapid relief of neurological signs and symptoms as well as enhanced local control. Unfortunately, WBI may also engender side effects including memory deficits and decrements in quality of life. Since memory control is thought to be mediated by the hippocampus, attention has been turned to whole brain radiotherapeutic techniques that allow sparing of the hippocampus. In order to be able to minimize dose deposition within the hippocampus, clinicians must be able to confidently identify that structure. However, manually tracing out the hippocampus for each patient is time consuming and subject to individual bias. To this end, an automated method can be very useful for such a task. In this paper, we present a method for extracting the hippocampus from magnetic resonance imaging (MRI) data. Our method is based on a multi-scale shape representation using statistical learning in conjunction with spherical wavelets for shape representation. Indeed, the hippocampus shape information is statistically learned by the algorithm and is further utilized to extract a hippocampus from the given 3D MR image. Results are shown on data-sets provided by Brigham and Women’s Hospital.


Radiotherapy planning Whole brain irradiation Hippocampus extraction Automatic segmentation 


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  1. 1.
    Patchell, R.: The management of brain metastases. Cancer Treat Rev. 29(6), 533–540 (2003)CrossRefGoogle Scholar
  2. 2.
    Knisely, J.: Focused attention on brain metastases. Lancet Oncol. 10, 1037–1044 (2009)CrossRefGoogle Scholar
  3. 3.
    Aoyama, H., Tago, M., Kato, N., et al.: Neurocognitive function of patients with brain metastasis who received either whole brain radiotherapy plus stereotactic radiosurgery or radiosurgery alone. In: Int. J. Radiat Oncol, vol. 68(5), pp. 1388–1395 (2007)Google Scholar
  4. 4.
    Chang, E., Wefel, J., Hess, K., et al.: Neurocognition in patients with brain metastases treated with radiosurgery or radiosurgery plus whole-brain irradiation: a randomised controlled trial. Lancet Oncology 10(11), 1037–1044 (2009)CrossRefGoogle Scholar
  5. 5.
    Cootes, T., Taylor, C., Cooper, D., et al.: Active shape models-their training and application. Comput. Vis. Image Und. 61(1), 38–59 (1995)CrossRefGoogle Scholar
  6. 6.
    Leventon, M., Grimson, W., Faugeras, O.: Statistical shape influence in geodesic active contours. In: IEEE Conference on CVPR (2000)Google Scholar
  7. 7.
    Tsai, A., Yezzi, A., Wells, W., et al.: A shape-based approach to the segmentation of medical imagery using level sets. IEEE T. Med. Imaging 22(2), 137–154 (2003)CrossRefGoogle Scholar
  8. 8.
    Rousson, M., Paragios, N., Deriche, R.: Implicit active shape models for 3D segmentation in MR imaging. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 209–216. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Staib, L., Duncan, J.: Model-based deformable surface finding for medical images. IEEE T. Med. Imaging 15(5), 720–731 (1996)CrossRefGoogle Scholar
  10. 10.
    Davatzikos, C., Tao, X., Shen, D.: Hierarchical active shape models, using the wavelet transform. IEEE T. Med. Imaging 22(3), 414–423 (2003)CrossRefGoogle Scholar
  11. 11.
    Nain, D., Haker, S., Bobick, A., et al.: Multiscale 3-D Shape Representation and Segmentation Using Spherical Wavelets. IEEE T. Med. Imaging 26(4), 598–618 (2007)CrossRefGoogle Scholar
  12. 12.
    Angenent, S., Haker, S., Tannenbaum, A., et al.: On the Laplace-Beltrami operator and brain surface flattening. IEEE T. Med. Imaging 18(8), 700–711 (1999)CrossRefGoogle Scholar
  13. 13.
    Whitaker, R.: A level-set approach to 3D reconstruction from range data. Int. J. Comput. Vision 29(3), 231 (1998)CrossRefGoogle Scholar
  14. 14.
    Donoho, D.: De-noising by soft-thresholding. IEEE T. Inform. Theory 41(3), 613–627 (1995)zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Rohlfing, T., Brandt, R., Menzel, R., et al.: Quo Vadis, Atlas-Based Segmentation? Handbook of Biomedical Image Analysis. In: Registration models, p. 435 (2005)Google Scholar
  16. 16.
    Wells, W., Viola, P., Atsumi, H., et al.: Multi-modal volume registration by maximization of mutual information. Med. Image Anal. 1(1), 35–51 (1996)CrossRefGoogle Scholar
  17. 17.
    Rueckert, D., Sonoda, L., Hayes, C., et al.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE T. Med. Imaging 18(8), 712–721 (1999)CrossRefGoogle Scholar
  18. 18.
    Lankton, S., Tannenbaum, A.: Localizing Region-Based Active Contours. IEEE T. Image Process 17(11), 2029–2039 (2008)CrossRefMathSciNetGoogle Scholar
  19. 19.
    Pasquier, D., Lacornerie, T., Vermandel, M., et al.: Automatic segmentation of pelvic structures from magnetic resonance images for prostate cancer radiotherapy. Int. J. Radiat. Oncol. 68(2), 592–600 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yi Gao
    • 1
  • Benjamin W. Corn
    • 2
  • Dan Schifter
    • 2
  • Allen Tannenbaum
    • 3
    • 4
  1. 1.School of Biomedical EngineeringGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Tel-Aviv Sourasky Medical CenterTel-AvivIsrael
  3. 3.Schools of Electrical & Computer Engineering and Biomedical EngineeringGeorgia Institute of TechnologyAtlantaUSA
  4. 4.Department of EETechnion-IITIsrael

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