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Within-brain classification for brain tumor segmentation

  • Mohammad HavaeiEmail author
  • Hugo Larochelle
  • Philippe Poulin
  • Pierre-Marc Jodoin
Original Article

Abstract

Purpose

In this paper, we investigate a framework for interactive brain tumor segmentation which, at its core, treats the problem of interactive brain tumor segmentation as a machine learning problem.

Methods

This method has an advantage over typical machine learning methods for this task where generalization is made across brains. The problem with these methods is that they need to deal with intensity bias correction and other MRI-specific noise. In this paper, we avoid these issues by approaching the problem as one of within brain generalization. Specifically, we propose a semi-automatic method that segments a brain tumor by training and generalizing within that brain only, based on some minimum user interaction.

Conclusion

We investigate how adding spatial feature coordinates (i.e., i, j, k) to the intensity features can significantly improve the performance of different classification methods such as SVM, kNN and random forests. This would only be possible within an interactive framework. We also investigate the use of a more appropriate kernel and the adaptation of hyper-parameters specifically for each brain.

Results

As a result of these experiments, we obtain an interactive method whose results reported on the MICCAI-BRATS 2013 dataset are the second most accurate compared to published methods, while using significantly less memory and processing power than most state-of-the-art methods.

Keywords

Within-brain generalization Machine learning Brain tumor segmentation Computer-aided detection Segmentation Interactive 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with human participants performed by any of the authors.

References

  1. 1.
    Bauer S, Nolte L, Reyes M (2011) Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization. In: Medical image computing and computer-assisted intervention. Springer, Berlin, pp 354–361Google Scholar
  2. 2.
    Bauer S, Wiest R, Nolte L, Reyes M (2013) A survey of MRI-based medical image analysis for brain tumor studies. Phys Med Biol 58(13):R97CrossRefPubMedGoogle Scholar
  3. 3.
    Cai H, Verma R, Ou Y, Lee S, Melhem E, Davatzikos C (2007) Probabilistic segmentation of brain tumors based on multi-modality magnetic resonance images. In: Biomedical imaging: from nano to macro, 2007. ISBI 2007. 4th IEEE international symposium on, IEEE, pp 600–603Google Scholar
  4. 4.
    Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297Google Scholar
  5. 5.
    Hamamci A, Unal G (2012) Multimodal brain tumor segmentation using the tumor-cut method on the brats dataset. In: Proceedings of the workshop on brain tumor segmentation, MICCAI pp 19–23Google Scholar
  6. 6.
    Hamamci A, Kucuk N, Karaman K, Engin K, Unal G (2012) Tumor-cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications. IEEE Trans Med Imaging 31(3):790–804CrossRefPubMedGoogle Scholar
  7. 7.
    Ho S, Bullitt E, Gerig G (2002) Level-set evolution with region competition: automatic 3-D segmentation of brain tumors. In: Proceedings of the international conference pattern recognition, vol 1, pp 532–535Google Scholar
  8. 8.
    Huo J, Okada K, van Rikxoort EM, Kim HJ, Alger JR, Pope WB, Goldin JG, Brown MS (2013) Ensemble segmentation for GBM brain tumors on MR images using confidence-based averaging. Med Phys 40(9):1CrossRefGoogle Scholar
  9. 9.
    Jiang C, Zhang X, Huang W, Meinel C (2004) Segmentation and quantification of brain tumor. In: Virtual environments, human-computer interfaces and measurement systems, 2004. (VECIMS). 2004 IEEE Symposium on, pp 61–66Google Scholar
  10. 10.
    Lampert CH (2009) Kernel methods in computer vision. Found Trends Comput Graph Vis 4(3):193–285CrossRefGoogle Scholar
  11. 11.
    Lee C, Wang S, Murtha A, Brown M, Greiner R (2008) Segmenting brain tumors using pseudo-conditional random fields. In: Medical image computing and computer-assisted intervention. Springer, Berlin, pp 359–366Google Scholar
  12. 12.
    Luts J, Heerschap A, Suykens J, Huffel SV (2007) A combined MRI and MRSI based multiclass system for brain tumour recognition using LS-SVMS with class probabilities and feature selection. Artif Intell Med 40(2):87–102CrossRefPubMedGoogle Scholar
  13. 13.
    Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R, Lanczi L, Gerstner E, Weber MA, Arbel T, Avants BB, Ayache N, Buendia P, Collins DL, Cordier N, Corso JJ, Criminisi A, Das T, Delingette H, Demiralp C, Durst CR, Dojat M, Doyle S, Festa J, Forbes F, Geremia E, Glocker B, Golland P, Guo X, Hamamci A, Iftekharuddin KM, Jena R, John NM, Konukoglu E, Lashkari D, Mariz JA, Meier R, Pereira S, Precup D, Price SJ, Riklin Raviv T, Reza SMS, Ryan M, Sarikaya D, Schwartz L, Shin HC, Shotton J, Silva C.A, Sousa N, Subbanna NK, Szekely G, Taylor TJ, Thomas OM, Tustison NJ, Unal G, Vasseur F, Wintermark M, Ye DH, Zhao L, Zhao B, Zikic D, Prastawa M, Reyes M, Van Leemput K (2015) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 34(10):1993–2024. doi: 10.1109/TMI.2014.2377694
  14. 14.
    Murphy K (2012) Machine learning: a probabilistic perspective. MIT Press, Cambridge, MAGoogle Scholar
  15. 15.
    Platt J (1999) Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in large margin classifiers, CiteseerGoogle Scholar
  16. 16.
    Prastawa M, Bullitt E, Ho S, Gerig G (2003) Robust estimation for brain tumor segmentation. In: Medical image computing and computer-assisted intervention—MICCAI 2003. Springer, Berlin, pp 530–537Google Scholar
  17. 17.
    Schmidt M, Levner I, Greiner R, Murtha A, Bistritz A (2005) Segmenting brain tumors using alignment-based features. In: International conference on machine learning and applications, p 6Google Scholar
  18. 18.
    Subbanna N, Precup D, Arbel T (2014) Iterative multilevel MRF leveraging context and voxel information for brain tumour segmentation in MRI. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Columbus, Ohio, pp 400–405Google Scholar
  19. 19.
    Vaidyanathan M, Clarke L, Velthuizen R, Phuphanich S, Bensaid A, Hall L, Bezdek J, Greenberg H, Trotti A, Silbiger M (1995) Comparison of supervised MRI segmentation methods for tumor volume determination during therapy. Magn Reson Imaging 13(5):719–728CrossRefPubMedGoogle Scholar
  20. 20.
    Wang T, Cheng I, Basu A (2009) Fluid vector flow and applications in brain tumor segmentation. IEEE Trans Biomed Eng 56(3):781–789CrossRefPubMedGoogle Scholar

Copyright information

© CARS 2015

Authors and Affiliations

  • Mohammad Havaei
    • 1
    Email author
  • Hugo Larochelle
    • 1
  • Philippe Poulin
    • 1
  • Pierre-Marc Jodoin
    • 1
  1. 1.Université de SherbrookeSherbrookeCanada

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