Advertisement

Automatic Segmentation of Intra-treatment CT Images for Adaptive Radiation Therapy of the Prostate

  • B. C. Davis
  • M. Foskey
  • J. Rosenman
  • L. Goyal
  • S. Chang
  • S. Joshi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3749)

Abstract

We have been developing an approach for automatically quantifying organ motion for adaptive radiation therapy of the prostate. Our approach is based on deformable image registration, which makes it possible to establish a correspondence between points in images taken on different days. This correspondence can be used to study organ motion and to accumulate inter-fraction dose. In prostate images, however, the presence of bowel gas can cause significant correspondence errors. To account for this problem, we have developed a novel method that combines large deformation image registration with a bowel gas segmentation and deflation algorithm. In this paper, we describe our approach and present a study of its accuracy for adaptive radiation therapy of the prostate. All experiments are carried out on 3-dimensional CT images.

Keywords

Planning Target Volume Image Registration Automatic Segmentation Manual Segmentation Organ Motion 
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.
    Yan, D., Lockman, D., Brabbins, D., Tyburski, L., Martinez, A.: An off-line strategy for constructing a patient-specific planning target volume in adaptive treatment process for prostate cancer. International Journal of Radiation Oncology*Biology*Physics 48, 289–302 (2000)CrossRefGoogle Scholar
  2. 2.
    van Herk, M., Bruce, A., Guus Kroes, A.P., Shouman, T., Touw, A., Lebesque, J.V.: Quantification of organ motion during conformal radiotherapy of the prostate by three dimensional image registration. International Journal of Radiation Oncology*Biology*Physics 33, 1311–1320 (1995)CrossRefGoogle Scholar
  3. 3.
    Ketting, C.H., Austin-Seymour, M., Kalet, I., Unger, J., Hummel, S., Jacky, J.: Consistency of three-dimensional planning target volumes across physicians and institutions. International Journal of Radiation Oncology*Biology*Physics 37, 445–453 (1997)CrossRefGoogle Scholar
  4. 4.
    Christensen, G.E., Carlson, B., Chao, K.S.C., Yin, P., Grigsby, P.W., Nguyen, K., Dempsey, J.F., Lerma, F.A., Bae, K.T., Vannier, M.W., Williamson, J.F.: Image-based dose planning of intracavitary brachytherapy: registration of serial-imaging studies using deformable anatomic templates. International Journal of Radiation Oncology*Biology*Physics 51, 227–243 (2001)Google Scholar
  5. 5.
    Schaly, B., Kempe, J.A., Bauman, G.S., Battista, J.J., Dyk, J.V.: Tracking the dose distribution in radiation therapy by accounting for variable anatomy. Physics in Medicine and Biology 49, 791–805 (2004)CrossRefGoogle Scholar
  6. 6.
    Lu, W., Chen, M., Olivera, G.H., Ruchala, K.J., Mackie, T.R.: Fast free-form deformable registration via calculus of variations. Physics in Medicine and Biology 49, 3067–3087 (2004)CrossRefGoogle Scholar
  7. 7.
    Wang, H., Dong, L., O’Daniel, J., Mohan, R., Garden, A.S., Ang, K.K., Kuban, D.A., Bonnen, M., Chang, J.Y., Cheung, R.: Validation of an accelerated ‘demons’ algorithm for deformable image registration in radiation therapy. Physics in Medicine and Biology 50, 2887–2905 (2005)CrossRefGoogle Scholar
  8. 8.
    Joshi, S., Lorenzen, P., Gerig, G., Bullitt, E.: Structural and radiometric asymmetry in brain images. Medical Image Analysis 7, 155–170 (2003)CrossRefGoogle Scholar
  9. 9.
    Miller, M.I., Joshi, S.C., Christensen, G.E.: Large deformation fluid diffeomorphisms for landmark and image matching. In: Toga, A.W. (ed.) Brain Warping. Academic Press, London (1999)Google Scholar
  10. 10.
    Christensen, G.E., Rabbitt, R.D., Miller, M.I.: Deformable templates using large deformation kinematics. IEEE Transactions On Image Processing 5, 1435–1447 (1996)CrossRefGoogle Scholar
  11. 11.
    Amenta, N., Choi, S., Kolluri, R.K.: The power crust. In: ACM Symposium on Solid Modeling and Applications, pp. 249–260 (2001)Google Scholar
  12. 12.
    Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26, 297–302 (1945)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • B. C. Davis
    • 1
    • 2
  • M. Foskey
    • 1
    • 2
  • J. Rosenman
    • 2
  • L. Goyal
    • 2
  • S. Chang
    • 2
  • S. Joshi
    • 1
    • 2
  1. 1.Department of Computer ScienceUniversity of North CarolinaUSA
  2. 2.Department of Radiation OncologyUniversity of North CarolinaUSA

Personalised recommendations