Advertisement

Anatomy-Guided Brain Tumor Segmentation and Classification

  • Bi SongEmail author
  • Chen-Rui Chou
  • Xiaojing Chen
  • Albert Huang
  • Ming-Chang Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10154)

Abstract

In this paper, we consider the problem of fully automatic brain tumor segmentation in multimodal magnetic resonance images. In contrast to applying classification on entire volume data, which requires heavy load of both computation and memory, we propose a two-stage approach. We first normalize image intensity and segment the whole tumor by utilizing the anatomy structure information. By dilating the initial segmented tumor as the region of interest (ROI), we then employ the random forest classifier on the voxels, which lie in the ROI, for multi-class tumor segmentation. Followed by a novel pathology-guided refinement, some mislabels of random forest can be corrected. We report promising results obtained using BraTS 2015 training dataset.

Keywords

Random Forest Hausdorff Distance Random Forest Classifier Initial Segmentation Tumor 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.

References

  1. 1.
    The Virtual Skeleton Database (VSD). www.virtualskeleton.ch
  2. 2.
    Bezdek, J.: A convergence theorem for the fuzzy ISODATA clustering algorithms. IEEE Trans. Pattern Anal. Mach. Intell. PAMI 2(1), 1–8 (1980)CrossRefzbMATHGoogle Scholar
  3. 3.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefzbMATHGoogle Scholar
  4. 4.
    Chang, P.D.: Fully convolutional neural networks with hyperlocal features for brain tumor segmentation. In: Proceedings MICCAI-BRATS Workshop 2016, pp. 4–9 (2016)Google Scholar
  5. 5.
    Dunn, J.: A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. J. Cybern. 3(3), 32–57 (1973)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Folgoc, L.L., Nori, A.V., Alvarez-Valle, J., Lowe, R., Criminisi, A.: Segmentation of brain tumors via cascades of lifted decision forests. In: Proceedings MICCAI-BRATS Workshop 2016, pp. 26–30 (2016)Google Scholar
  7. 7.
    Geremia, E., Clatz, O., Menze, B.H., Konukoglu, E., Criminisi, A., Ayache, N.: Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images. In: Proceedings of MICCAI (2011)Google Scholar
  8. 8.
    Kamnitsas, K., Ferrante, E., Parisot, S., Ledig, C., Nori, A., Criminisi, A., Rueckert, D., Glocker, B.: DeepMedic on brain tumor segmentation. In: Proceedings MICCAI-BRATS Workshop 2016, pp. 18–22 (2016)Google Scholar
  9. 9.
    Kleesiek, J., Biller, A., Urban, G., Köthe, UGoogle Scholar
  10. 10.
    Meier, R., Knecht, U., Wiest, R., Reyes, M.: CRF-based brain tumor segmentation: alleviating the shrinking bias. In: Proceedings MICCAI-BRATS Workshop 2016, pp. 35–39 (2016)Google Scholar
  11. 11.
    Menze, B.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)CrossRefGoogle Scholar
  12. 12.
    Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y., Egab, A., Yushkevich, P.A., Gee, J.C.: N4ITK: Improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)CrossRefGoogle Scholar
  13. 13.
    Tustison, N., Wintermark, M., Durst, C., Avants, B.: ANTs and Árboles. In: Proceedings MICCAI-BRATS Workshop 2013, pp. 47–50 (2013)Google Scholar
  14. 14.
    Vezhnevets, V., Konouchine, V.: “GrowCut” - interactive multi-label N-D image segmentation by cellular automata. In: Proceedings of GraphiCon, pp. 150–156 (2005)Google Scholar
  15. 15.
    Zeng, K., Bakas, S., Sotiras, A., Akbari, H., Rozycki, M., Rathore, S., Pati, S., Davatzikos, C.: Segmentation of gliomas in pre-operative and post-operative multimodal magnetic resonance imaging volumes based on a hybrid generative-discriminative framework. In: Proceedings MICCAI-BRATS Workshop 2016, pp. 60–67 (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Bi Song
    • 1
    Email author
  • Chen-Rui Chou
    • 1
  • Xiaojing Chen
    • 1
  • Albert Huang
    • 1
  • Ming-Chang Liu
    • 1
  1. 1.US Research Center, Sony Electronics Inc.San JoseUSA

Personalised recommendations