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Finding a Path for Segmentation Through Sequential Learning

  • Hongzhi WangEmail author
  • Yu Cao
  • Tanveer F. Syed-Mahmood
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9123)

Abstract

Sequential learning techniques, such as auto-context, that applies the output of an intermediate classifier as contextual features for its subsequent classifier has shown impressive performance for semantic segmentation. We show that these methods can be interpreted as an approximation technique derived from a Bayesian formulation. To improve the effectiveness of applying this approximation technique, we propose a new sequential learning approach for semantic segmentation that solves a segmentation problem by breaking it into a series of simplified segmentation problems. Sequentially solving each of the simplified problems along the path leads to a more effective way for solving the original segmentation problem. To achieve this goal, we also propose a learning-based method to generate simplified segmentation problems by explicitly controlling the complexities of the modeling classifiers. We report promising results on the 2013 SATA canine leg muscle segmentation dataset.

References

  1. 1.
    Asman, A., Akhondi-Asl, A., Wang, H., Tustison, N., Avants, B., Warfield, S.K., Landman, B.: MICCAI 2013 segmentation algorithms, theory and applications (SATA) challenge results summary. In: MICCAI 2013 Challenge Workshop on Segmentation: Algorithms, Theory and Applications. Springer (2013)Google Scholar
  2. 2.
    Avants, B., Epstein, C., Grossman, M., Gee, J.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26–41 (2008)CrossRefGoogle Scholar
  3. 3.
    Cohen, W.W., Carvalho, V.R.: Stacked sequential learning. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence, Edinburgh, Scotland, pp. 671–676 (2005)Google Scholar
  4. 4.
    Freund, Y., Schapire, R.E.: A desicion-theoretic generalization of on-line learning and an application to boosting. In: Vitányi, P.M.B. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–37. Springer, Heidelberg (1995) CrossRefGoogle Scholar
  5. 5.
    Heckemann, R., Hajnal, J., Aljabar, P., Rueckert, D., Hammers, A.: Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage 33, 115–126 (2006)CrossRefGoogle Scholar
  6. 6.
    Montillo, A., Shotton, J., Winn, J., Iglesias, J.E., Metaxas, D., Criminisi, A.: Entangled decision forests and their application for semantic segmentation of CT images. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 184–196. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  7. 7.
    Munoz, D., Bagnell, J.A., Hebert, M.: Stacked hierarchical labeling. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part VI. LNCS, vol. 6316, pp. 57–70. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  8. 8.
    Rohlfing, T., Brandt, R., Menzel, R., Russakoff, D.B., Maurer Jr., C.R.: Quo vadis, atlas-based segmentation? In: Suri, J.S., Wilson, D.L., Laxminarayan, S. (eds.) Volume III: Registration Models. Topics in Biomedical Engineering International Book Series, pp. 435–486. Springer, US (2005) CrossRefGoogle Scholar
  9. 9.
    Tu, Z., Bai, X.: Auto-context and its application to high-level vision tasks and 3D brain image segmentation. IEEE Trans. on PAMI 32(10), 1744–1757 (2010)CrossRefGoogle Scholar
  10. 10.
    Tu, Z., Zheng, S., Yuille, A., Reiss, A., Dutton, R., Lee, A., Galaburda, A., Dinov, I., Thompson, P., Toga, A.: Automated extraction of the cortical sulci based on a supervised learning approach. IEEE TMI 26(4), 541–552 (2007)Google Scholar
  11. 11.
    Van Leemput, K., Benner, T., Bakkour, A., Wiggins, G., Wald, L., Augustinack, J., Dickerson, B., Golland, P., Fischl, B.: Automated segmentation of hippocampal subfields from ultra-high resolution in vivo mri. Hippocampus 19, 549–557 (2009)CrossRefGoogle Scholar
  12. 12.
    Wang, H., Suh, J.W., Das, S., Pluta, J., Craige, C., Yushkevich, P.: Multi-atlas segmentation with joint label fusion. IEEE Trans. on PAMI 35(3), 611–623 (2013)CrossRefGoogle Scholar
  13. 13.
    Wang, H., Das, S.R., Suh, J.W., Altinay, M., Pluta, J., Craige, C., Avants, B.B., Yushkevich, P.A.: A learning-based wrapper method to correct systematic errors in automatic image segmentation: Consistently improved performance in hippocampus, cortex and brain. Neuroimage 55(3), 968–985 (2011)CrossRefGoogle Scholar
  14. 14.
    Wang, H., Yushkevich, P.A.: Multi-atlas segmentation with joint label fusion and corrective learning - an open source implementation. Front. neuroinformatics 7, 27 (2013)Google Scholar
  15. 15.
    Wolpert, D.H.: Stacked generalization. Neural netw. 5(2), 241–259 (1992)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Hongzhi Wang
    • 1
    Email author
  • Yu Cao
    • 1
  • Tanveer F. Syed-Mahmood
    • 1
  1. 1.IBM Almaden Research CenterSan JoseUSA

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