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Supervised Image Segmentation across Scanner Protocols: A Transfer Learning Approach

  • Annegreet van Opbroek
  • M. Arfan Ikram
  • Meike W. Vernooij
  • Marleen de Bruijne
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7588)

Abstract

Supervised classification techniques are among the most powerful methods used for automatic segmentation of medical images. A disadvantage of these methods is that they require a representative training set and thus encounter problems when the training data is acquired e.g. with a different scanner protocol than the target segmentation data. We therefore propose a framework for supervised biomedical image segmentation across different scanner protocols, by means of transfer learning. We establish a transfer learning algorithm for classification, which can exploit a large amount of labeled samples from different sources in addition to a small amount of samples from the target source. The algorithm iteratively re-weights the contribution of training samples from these different sources based on classification by a weighted SVM classifier. We evaluate this technique by performing tissue classification on MRI brain data from four substantially different scanning protocols. For a small number of labeled samples from a single image obtained with the same protocol, the proposed transfer learning method outperforms classification on all available training data as well as classification based on the labeled target samples only. The classification errors for these cases can be reduced with up to 40 percent compared to traditional classification techniques.

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References

  1. 1.
    Fischl, B., Salat, D., van der Kouwe, A., Makris, N., Ségonne, F., Quinn, B., Dale, A.: Sequence-independent segmentation of magnetic resonance images. Neuroimage 23, S69–S84 (2004)Google Scholar
  2. 2.
    Cocosco, C., Zijdenbos, A., Evans, A.: A fully automatic and robust brain MRI tissue classification method. Medical Image Analysis 7(4), 513–527 (2003)CrossRefGoogle Scholar
  3. 3.
    Mayer, A., Greenspan, H.: An adaptive mean-shift framework for MRI brain segmentation. IEEE Transactions on Medical Imaging 28(8), 1238–1250 (2009)CrossRefGoogle Scholar
  4. 4.
    Grabowski, T., Frank, R., Szumski, N., Brown, C., Damasio, H.: Validation of partial tissue segmentation of single-channel magnetic resonance images of the brain. NeuroImage 12(6), 640–656 (2000)CrossRefGoogle Scholar
  5. 5.
    Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based tissue classification of MR images of the brain. IEEE Transactions on Medical Imaging 18(10), 897–908 (1999)CrossRefGoogle Scholar
  6. 6.
    Pan, S., Yang, Q.: A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22(10), 1345–1359 (2010)CrossRefGoogle Scholar
  7. 7.
    Dai, W., Yang, Q., Xue, G., Yu, Y.: Boosting for transfer learning. In: Proceedings of the 24th International Conference on Machine Learning, pp. 193–200. ACM (2007)Google Scholar
  8. 8.
    Chang, C., Lin, C.: Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST) 2(3), 27 (2011)Google Scholar
  9. 9.
    Hofman, A., Breteler, M., Van Duijn, C., Janssen, H., Krestin, G., Kuipers, E., Stricker, B., Tiemeier, H., Uitterlinden, A., Vingerling, J., et al.: The Rotterdam Study: 2010 objectives and design update. European Journal of Epidemiology 24(9), 553–572 (2009)CrossRefGoogle Scholar
  10. 10.
    Worth, A.: The Internet Brain Segmentation Repository (IBSR)Google Scholar
  11. 11.
    Sled, J., Zijdenbos, A., Evans, A.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging 17(1), 87–97 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Annegreet van Opbroek
    • 1
  • M. Arfan Ikram
    • 2
  • Meike W. Vernooij
    • 2
  • Marleen de Bruijne
    • 1
    • 3
  1. 1.Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and RadiologyErasmus MC - University Medical Center RotterdamThe Netherlands
  2. 2.Departments of Epidemiology and RadiologyErasmus MC - University Medical Center RotterdamThe Netherlands
  3. 3.Department of Computer ScienceUniversity of CopenhagenDenmark

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