Advertisement

Fast Automatic Multi-atlas Segmentation of the Prostate from 3D MR Images

  • Jason A. Dowling
  • Jurgen Fripp
  • Shekhar Chandra
  • Josien P W. Pluim
  • Jonathan Lambert
  • Joel Parker
  • James Denham
  • Peter B. Greer
  • Olivier Salvado
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6963)

Abstract

A fast fully automatic method of segmenting the prostate from 3D MR scans is presented, incorporating dynamic multi-atlas label fusion. The diffeomorphic demons method is used for non-rigid registration and a comparison of alternate metrics for atlas selection is presented. A comparison of results from an average shape atlas and the multi-atlas approach is provided. Using the same clinical dataset and manual contours from 50 clinical scans as Klein et al. (2008) a median Dice similarity coefficient of 0.86 was achieved with an average surface error of 2.00mm using the multi-atlas segmentation method.

Keywords

Mean Root Square Difference Dice Similarity Coefficient Probabilistic Atlas Pairwise Registration Prostate Segmentation 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Karlsson, M., Karlsson, M.G., Nyholm, T., Amies, C., Zackrisson, B.: Dedicated magnetic resonance imaging in the radiotherapy clinic. Int. J. Radiat. Oncol. Biol. Phys. 74, 644–651 (2009)CrossRefGoogle Scholar
  2. 2.
    Raaymakers, B.W., Lagendijk, J.J.W., Overweg, J., Kok, J.G.M., Raaijmakers, A.J.E., Kerkhof, E.M., van der Put, R.W., Meijsing, I., Crijns, S.P.M., Benedosso, F., van Vulpen, M., de Graaff, C.H.W., Allen, J., Brown, K.J.: Integrating a 1.5 T MRI scanner with a 6 MV accelerator: proof of concept. Phys. Med. Biol. 54, N229–N237, (2009)CrossRefGoogle Scholar
  3. 3.
    Prabhakar, R., Julka, P.K., Ganesh, T., Munshi, A., Joshi, R.C., Rath, G.K.: Feasibility of using MRI alone for 3D Radiation Treatment Planning in Brain Tumors. Japanese Journal of Clinical Oncology 37, 405–411 (2007)CrossRefGoogle Scholar
  4. 4.
    Roach, M., Faillace-Akazawa, P., Malfatti, C., Holland, J., Hricak, H.: Prostate volumes defined by magnetic resonance imaging and computerized tomographic scans for three-dimensional conformal radiotherapy. Int. J. Radiat. Oncol. Biol. Phys. 35, 1011–1018 (1996)CrossRefGoogle Scholar
  5. 5.
    Rasch, C., Barillot, I., Remeijer, P., Touw, A., Herk, M., van Lebesque, J.V.: Definition of the prostate in CT and MRI: a multi-observer study. International Journal of Radiation Oncology Biology Physics 43, 57–66 (1999)CrossRefGoogle Scholar
  6. 6.
    Rohlfing, T., Brandt, R., Menzel, R., Maurer, C.R.: Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. Neuroimage 21, 1428–1442 (2004)CrossRefGoogle Scholar
  7. 7.
    Martin, S., Troccaz, J., Daanenc, V.: Automated segmentation of the prostate in 3D MR images using a probabilistic atlas and a spatially constrained deformable model. Med. Phys. 37, 1579–1590 (2010)CrossRefGoogle Scholar
  8. 8.
    Klein, S., Heide, U.A., van der Lips, I.M., Vulpen, M., van Staring, M., Pluim, J.P.W.: Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Medical Physics 35, 1407–1417 (2008)CrossRefGoogle Scholar
  9. 9.
    Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Transactions on Medical Imaging 18, 712–721 (1999)CrossRefGoogle Scholar
  10. 10.
    Dowling, J., Lambert, J., Parker, J., Greer, P.B., Fripp, J., Denham, J., Ourselin, S., Salvado, O.: Automatic MRI atlas-based external beam radiation therapy treatment planning for prostate cancer. In: Madabhushi, A., et al. (eds.) MICCAI 2010. LNCS, vol. 6369, pp. 25–33. Springer, Heidelberg (2010)Google Scholar
  11. 11.
    Greer, P.B., Dowling, J.A., Lambert, J.A., Fripp, J., Parker, J., Denham, J.W., Wratten, C., Capp, A., Salvado, O.: A magnetic resonance imaging-based workflow for planning radiation therapy for prostate cancer. The Medical Journal of Australia 194, S24–S27 (2011)Google Scholar
  12. 12.
    Lambert, J., Greer, P.B., Menk, F., Patterson, J., Parker, J., Capp, A., Wratten, C., Dowling, J., Salvado, O., Hughes, C., Fisher, K., Ostwald, P., Denham, J.W.: MRI-guided prostate radiation therapy planning: Investigation of dosimetric accuracy of MRI-based dose planning. Radiotherapy and Oncology 98, 330–334 (2011)CrossRefGoogle Scholar
  13. 13.
    Warfield, S.K., Zou, K.H., Wells, W.M.: Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Transactions on Medical Imaging 23, 903–921 (2004)CrossRefGoogle Scholar
  14. 14.
    Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y., Egan, A., Yushkevich, P.A., Gee, J.C.: N4ITK: Improved N3 Bias Correction. IEEE Transactions on Medical Imaging 29, 1310–1320 (2010)CrossRefGoogle Scholar
  15. 15.
    Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Non-parametric Diffeomorphic Image Registration with the Demons Algorithm. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 319–326. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  16. 16.
    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 2011

Authors and Affiliations

  • Jason A. Dowling
    • 1
  • Jurgen Fripp
    • 1
  • Shekhar Chandra
    • 1
  • Josien P W. Pluim
    • 2
  • Jonathan Lambert
    • 3
    • 4
  • Joel Parker
    • 3
  • James Denham
    • 3
    • 4
  • Peter B. Greer
    • 3
    • 4
  • Olivier Salvado
    • 1
  1. 1.CSIRO ICT CentreAustralian e-Health Research CentreAustralia
  2. 2.University Medical Center UtrechtThe Netherlands
  3. 3.Calvary Mater Newcastle HospitalAustralia
  4. 4.University of NewcastleAustralia

Personalised recommendations