Integrated Spatio-Temporal Segmentation of Longitudinal Brain Tumor Imaging Studies

  • Stefan Bauer
  • Jean Tessier
  • Oliver Krieter
  • Lutz-P. Nolte
  • Mauricio Reyes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8331)

Abstract

Consistent longitudinal segmentation of brain tumor images is a critical issue in treatment monitoring and in clinical trials. Fully automatic segmentation methods are a good candidate for reliably detecting changes of tumor volume over time. We propose an integrated 4D spatio-temporal brain tumor segmentation method, which combines supervised classification with conditional random field regularization in an energy minimization scheme. Promising results and improvements over classic 3D methods for monitoring the temporal volumetric evolution of necrotic, active and edema tumor compartments are demonstrated on a longitudinal dataset of glioma patient images from a multi-center clinical trial. Thanks to its speed and simplicity the approach is a good candidate for standard clinical use.

Keywords

Brain tumor Glioma Longitudinal studies Segmentation Volumetric analysis 

Notes

Acknowledgements

We would like to thank Roche for providing the image data including the manual measurements. This research was partially funded by the Swiss Institute for Computer Assisted Surgery (SICAS), the Swiss Cancer League and the Bernese Cancer League.

References

  1. 1.
    Ananthnarayan, S., Bahng, J., Roring, J., Nghiemphu, P., Lai, A., Cloughesy, T., Pope, W.B.: Time course of imaging changes of GBM during extended bevacizumab treatment. J. Neuro-Oncology 88(3), 339–347 (2008)CrossRefGoogle Scholar
  2. 2.
    Angelini, E., Delon, J., Bah, A.B., Capelle, L., Mandonnet, E.: Differential MRI analysis for quantification of low grade glioma growth. Med. Image Anal. 16(1), 114–126 (2012)CrossRefGoogle Scholar
  3. 3.
    Bauer, S., Fejes, T., Slotboom, J., Wiest, R., Nolte, L.P., Reyes, M.: Segmentation of brain tumor images based on integrated hierarchical classification and regularization. In: Menze, B., Jakab, A., Bauer, S., Reyes, M., Prastawa, M., Van Leemput, K. (eds.) Miccai Brats Workshop. Miccai Society, Nice (2012)Google Scholar
  4. 4.
    Bauer, S., Nolte, L.-P., Reyes, M.: Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 354–361. Springer, Heidelberg (2011)Google Scholar
  5. 5.
    Bauer, S., Wiest, R., Nolte, L.P., Reyes, M.: A survey of MRI-based medical image analysis for brain tumor studies. Phys. Med. Biol. 58(13), R97–R129 (2013)CrossRefGoogle Scholar
  6. 6.
    Corso, J.J., Sharon, E., Dube, S., El-Saden, S., Sinha, U., Yuille, A.: Efficient multilevel brain tumor segmentation with integrated bayesian model classification. IEEE Trans. Med. Imaging 27(5), 629–640 (2008)CrossRefGoogle Scholar
  7. 7.
    Criminisi, A., Shotton, J., Konukoglu, E.: Decision forests for classification, regression, density estimation. manifold learning and semi-supervised learning. Tech. rep., Microsoft Research (2011)Google Scholar
  8. 8.
    Gooya, A., Pohl, K.M., Bilello, M., Cirillo, L., Biros, G., Melhem, E.R., Davatzikos, C.: GLISTR: glioma image segmentation and registration. IEEE Trans. Med. Imaging 31(10), 1941–1954 (2012)CrossRefGoogle Scholar
  9. 9.
    Henson, J.W., Ulmer, S., Harris, G.J.: Brain tumor imaging in clinical trials. AJNR. Am. J. Neuroradiol. 29(3), 419–424 (2008)CrossRefGoogle Scholar
  10. 10.
    Komodakis, N., Tziritas, G., Paragios, N.: Performance vs computational efficiency for optimizing single and dynamic MRFs: setting the state of the art with primal-dual strategies. Comput. Vis. Image Underst. 112(1), 14–29 (2008)CrossRefGoogle Scholar
  11. 11.
    Konukoglu, E., Wells, W., Novellas, S., Ayache, N., Kikinis, R., Black, P., Pohl, K.: Monitoring slowly evolving tumors. In: IEEE ISBI 2008, pp. 812–815. IEEE (2008)Google Scholar
  12. 12.
    Mazzara, G.P., Velthuizen, R.P., Pearlman, J.L., Greenberg, H.M., Wagner, H.: Brain tumor target volume determination for radiation treatment planning through automated MRI segmentation. Int. J. Radiat. Oncol. Biol. Phys. 59(1), 300–312 (2004)CrossRefGoogle Scholar
  13. 13.
    Pohl, K.M., Konukoglu, E., Novellas, S., Ayache, N., Fedorov, A., Talos, I.F., Golby, A., Wells, W.M., Kikinis, R., Black, P.M.: A new metric for detecting change in slowly evolving brain tumors: validation in meningioma patients. Neurosurgery 68(1 Suppl Operative), 225–233 (2011)Google Scholar
  14. 14.
    Wang, Y., Loe, K.F., Wu, J.K.: A dynamic conditional random field model for foreground and shadow segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 28(2), 279–289 (2006)CrossRefGoogle Scholar
  15. 15.
    Wen, P.Y., Macdonald, D.R., Reardon, D.A., Cloughesy, T.F., Sorensen, A.G., Galanis, E., Degroot, J., Wick, W., Gilbert, M.R., Lassman, A.B., Tsien, C., Mikkelsen, T., Wong, E.T., Chamberlain, M.C., Stupp, R., Lamborn, K.R., Vogelbaum, M.A., van den Bent, M.J., Chang, S.M.: Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J. Clin. Oncol.: Official J. Am. Soc. Clin. Oncol. 28(11), 1963–1972 (2010)CrossRefGoogle Scholar
  16. 16.
    Zikic, D., et al.: Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 369–376. Springer, Heidelberg (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Stefan Bauer
    • 1
  • Jean Tessier
    • 2
  • Oliver Krieter
    • 3
  • Lutz-P. Nolte
    • 1
  • Mauricio Reyes
    • 1
  1. 1.ISTBUniversity of BernBernSwitzerland
  2. 2.F. Hoffmann-La Roche Ltd.BaselSwitzerland
  3. 3.Roche Diagnostics GmbHPenzbergGermany

Personalised recommendations