Advertisement

CRF-Based Brain Tumor Segmentation: Alleviating the Shrinking Bias

  • Raphael MeierEmail author
  • Urspeter Knecht
  • Roland Wiest
  • Mauricio Reyes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10154)

Abstract

This paper extends a previously published brain tumor segmentation method with a dense Conditional Random Field (CRF). Dense CRFs can overcome the shrinking bias inherent to many grid-structured CRFs. We focus on illustrating the impact of alleviating the shrinking bias on the performance of CRF-based brain tumor segmentation. The proposed segmentation method is evaluated using data from the MICCAI BRATS 2013 & 2015 data sets (up to 110 patient cases for testing) and compared to a baseline method using a grid-structured CRF. Improved segmentation performance for the complete and enhancing tumor was observed with respect to grid-structured CRFs.

Notes

Acknowledgments

This project has received funding from the European Unions Seventh Framework Programme for research, technological development and demonstration under grant agreement Nº600841.

References

  1. 1.
    Bauer, S., Fejes, T., Reyes, M.: A skull-stripping filter for ITK. Insight J. 1–7 (2012). http://hdl.handle.net/10380/3353
  2. 2.
    Criminisi, A., Shotton, J.: Decision Forests for Computer Vision and Medical Image Analysis. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  3. 3.
    von Deimling, A.: Gliomas. Recent Results in Cancer Research, vol. 171. Springer, Heidelberg (2009). http://link.springer.com/10.1007/978-3-540-31206-2 CrossRefGoogle Scholar
  4. 4.
    Ellingson, B.M., Bendszus, M., Boxerman, J., Barboriak, D., Erickson, B.J., Smits, M., Nelson, S.J., Gerstner, E., Alexander, B., Goldmacher, G., Wick, W., Vogelbaum, M., Weller, M., Galanis, E., Kalpathy-Cramer, J., Shankar, L., Jacobs, P., Pope, W.B., Yang, D., Chung, C., Knopp, M.V., Cha, S., Van Den Bent, M.J., Chang, S., Al Yung, W.K., Cloughesy, T.F., Wen, P.Y., Gilbert, M.R., Whitney, A., Sandak, D., Musella, A., Haynes, C., Wallace, M., Arons, D.F., Kingston, A.: Consensus recommendations for a standardized brain tumor imaging protocol in clinical trials. Neuro Oncol. 17(9), 1188–1198 (2015)Google Scholar
  5. 5.
    Kohli, P., Ladický, L., Torr, P.H.S.: Robust higher order potentials for enforcing label consistency. Int. J. Comput. Vis. 82(3), 302–324 (2009)CrossRefGoogle Scholar
  6. 6.
    Komodakis, N., Tziritas, G.: Approximate labeling via graph cuts based on linear programming. IEEE Trans. Pattern Anal. Mach. Intell. 29(8), 1436–1453 (2007)CrossRefGoogle Scholar
  7. 7.
    Kraehenbuehl, P., Koltun, V.: Parameter learning and convergent inference for dense random fields. In: Proceedings of the 30th International Conference on Machine Learning (ICML 2013), vol. 28, pp. 513–521 (2013). http://machinelearning.wustl.edu/mlpapers/papers/icml2013_kraehenbuehl13
  8. 8.
    Krähenbühl, P., Koltun, V.: Efficient inference in fully connected CRFS with Gaussian edge potentials. In: NIPS 2011, pp. 109–117 (2011). http://arxiv.org/abs/1210.5644
  9. 9.
    Ladick, L.U., Russell, C., Kohli, P., Torr, P.H.S.: Associative hierarchical CRFs for object class image segmentation. In: ICCV (2009). http://cms.brookes.ac.uk/research/visiongroup/, http://research.microsoft.com/en-us/um/people/pkohli/
  10. 10.
    Meier, R., Bauer, S., Slotboom, J., Wiest, R., Reyes, M.: Appearance-and context-sensitive features for brain tumor segmentation. In: MICCAI BRATS Challenge (2014)Google Scholar
  11. 11.
    Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., Lanczi, L., Gerstner, E., Weber, M.A., Arbel, T., Avants, B.B., Ayache, N., Buendia, P., Louis Collins, D., Cordier, N., Corso, J.J., Criminisi, A., Das, T., Delingette, H., Demiralp, G., Durst, C.R., Dojat, M., Doyle, S., Festa, J., Forbes, F., Geremia, E., Glocker, B., Golland, P., Guo, X., Hamamci, A., Iftekharuddin, K.M., Jena, R., John, N.M., Konukoglu, E., Lashkari, D., António Mariz, J., Meier, R., Pereira, S., Precup, D., Price, S.J., Riklin Raviv, T., Reza, S.M., Ryan, M., Sarikaya, D., Schwartz, L., Shin, H.C., Shotton, J., Silva, C.A., Sousa, N., Subbanna, N.K., Szekely, G., Taylor, T.J., Thomas, O.M., Tustison, N.J., Unal, G., Vasseur, F., Wintermark, M., Hye Ye, D., Zhao, L., Zhao, B., Zikic, D., Prastawa, M., Reyes, M., Van Leemput, K., Golland, P., Lashkari, D., Guo, X., Schwartz, L., Zhao, B.: The multimodal brain tumor image segmentation benchmark (BRATS). TMI 34, 1993–2024 (2015). http://www.ieee.org/publications_standards/publications/rights/index.html
  12. 12.
    Nyul, L.G., Udupa, J.K., Zhang, X.: New variants of a method of MRI scale standardization. IEEE Trans. Med. Imaging 19(2), 143–150 (2000)CrossRefGoogle Scholar
  13. 13.
    Orlando, J., Prokofyeva, E., Blaschko, M.: A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images. IEEE Trans. Biomed. Eng. 1 (2016). http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7420682
  14. 14.
    Pantofaru, C., Schmid, C., Hebert, M.: Object recognition by integrating multiple image segmentations. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5304, pp. 481–494. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-88690-7_36 CrossRefGoogle Scholar
  15. 15.
    Pope, W.B., Sayre, J., Perlina, A., Villablanca, J.P., Mischel, P.S., Cloughesy, T.F.: MR imaging correlates of survival in patients with high-grade gliomas. Am. J. Neuroradiol. 26(10), 2466–2474 (2005)Google Scholar
  16. 16.
    Porz, N., Bauer, S., Pica, A., Schucht, P., Beck, J., Verma, R.K., Slotboom, J., Reyes, M., Wiest, R.: Multi-modal glioblastoma segmentation: man versus machine. PLoS ONE 9(5), e96873 (2014)CrossRefGoogle Scholar
  17. 17.
    R Core Team: R: A language and environment for statistical computing (2013). http://www.r-project.org/
  18. 18.
    Rios Velazquez, E., Meier, R., Dunn, W., Alexander, B., Wiest, R., Bauer, S., Gutman, D., Reyes, M., Aerts, H.: Fully automatic GBM segmentation in the TCGA-GBM dataset: prognosis and correlation with VASARI features. Sci. Rep. 42(6), 3589 (2015). http://www.ncbi.nlm.nih.gov/pubmed/26128818
  19. 19.
    Shelhamer, E., Jegelka, S., Darrell, T.: Communal cuts : sharing cuts across images. In: NIPS Workshop on Discrete and Combinatorial Problems in Machine Learning (DISCML), pp. 1–6 (2014)Google Scholar
  20. 20.
    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 Trans. Med. Imaging 29(6), 1310–1320 (2010)Google Scholar
  21. 21.
    Wang, C., Komodakis, N., Paragios, N.: Markov random field modeling, inference & learning in computer vision & image understanding: a survey. Comput. Vis. Image Underst. 117(11), 1610–1627 (2013). http://dx.doi.org/10.1016/j.cviu.2013.07.004
  22. 22.

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Raphael Meier
    • 1
    Email author
  • Urspeter Knecht
    • 2
  • Roland Wiest
    • 2
  • Mauricio Reyes
    • 1
  1. 1.Institute for Surgical Technology and BiomechanicsUniversity of BernBernSwitzerland
  2. 2.Support Center for Advanced Neuroimaging – Institute for Diagnostic and Interventional NeuroradiologyUniversity Hospital and University of BernBernSwitzerland

Personalised recommendations