Online Discriminative Multi-atlas Learning for Isointense Infant Brain Segmentation

  • Xuchu Wang
  • Li Wang
  • Heung-Il Suk
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8679)


Multi-atlas labeling in a non-local patch manner has emerged as an important approach to alleviate both the possible misalignment and mis-match among patches for guiding accurate image segmentation. However, the relationship among candidate patches and their intra/inter-class variability are less investigated, which limits the discriminative power of these patches. To address these issues, we present a new online discriminative multi-atlas learning method for labeling the target patch by the best representative candidates in a sparse sense. Specifically, the online multi-kernel learning is firstly adopted to map the patches into a cascade of discriminative kernel spaces for producing corresponding probability maps to model a label of each sample in these spaces. Then the online discriminative dictionary learning is proposed to build the atlas that handles the intra-class compactness and inter-class separability simultaneously. Finally, sparse coding is used to select patches in the dictionary for label propagation. In this way, the multi-atlas information dynamically learned with the context probability maps is iteratively incorporated to build the atlas dictionary, for gradually excluding the misleading candidate patches. The proposed method is validated by experiments on isointense infant brain tissue segmentation, and achieves promising results in comparison with several different labeling strategies.


Sparse Representation Sparse Code Dictionary Learning Target Patch Label Fusion 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wang, L., Shi, F., Li, G., Lin, W., Gilmore, J.H., Shen, D.: Integration of sparse multi-modality representation and geometrical constraint for isointense infant brain segmentation. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part I. LNCS, vol. 8149, pp. 703–710. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  2. 2.
    Wang, L., Shi, F., Li, G., Gao, Y., Lin, W., Gilmore, J.H., Shen, D.: Segmentation of neonatal brain MR images using patch-driven level sets. NeuroImage 84, 141–158 (2014)CrossRefGoogle Scholar
  3. 3.
    Warfield, S.K., Zou, K.H., Wells III, W.M.: Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans. Med. Imaging 23(7), 903–921 (2004)CrossRefGoogle Scholar
  4. 4.
    Heckemann, R.A., Hajnal, J.V., Aljabar, P., Rueckert, D., Hammers, A.: Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage 33(1), 115–126 (2006)CrossRefGoogle Scholar
  5. 5.
    Coupé, P., Manjón, J.V., Fonov, V., Pruessner, J.C., Robles, M., Collins, D.L.: Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage 54(2), 940–954 (2011)CrossRefGoogle Scholar
  6. 6.
    Rousseau, F., Habas, P.A., Studholme, C.: A supervised patch-based approach for human brain labeling. IEEE Trans. Med. Imaging 30(10), 1852–1862 (2011)CrossRefGoogle Scholar
  7. 7.
    Wang, H., Suh, J.W., Das, S.R., Pluta, J., Craige, C., Yushkevich, P.A.: Multi-atlas segmentation with joint label fusion. IEEE Trans. PAMI 35(3), 611–623 (2013)CrossRefGoogle Scholar
  8. 8.
    Wu, G., Wang, Q., Liao, S., Zhang, D., Nie, F., Shen, D.: Minimizing joint risk of mislabeling for iterative patch-based label fusion. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part III. LNCS, vol. 8151, pp. 551–558. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  9. 9.
    Wu, G., Wang, Q., Zhang, D., Nie, F., Huang, H., Shen, D.: A generative probability model of joint label fusion for multi-atlas based brain segmentation. Medical Image Analysis 18(6), 881–890 (2014)CrossRefGoogle Scholar
  10. 10.
    Bai, W., Shi, W., O’Regan, D.P., Tong, T., Wang, H., Jamil-Copley, S., Peters, N.S., Rueckert, D.: A probabilistic patch-based label fusion model for multi-atlas segmentation with registration refinement: Application to cardiac MR images. IEEE Trans. Med. Imaging 32(7), 1302–1315 (2013)CrossRefGoogle Scholar
  11. 11.
    Tong, T., Wolz, R., Coupé, P., Hajnal, J.V., Rueckert, D.: Segmentation of MR images via discriminative dictionary learning and sparse coding: Application to hippocampus labeling. NeuroImage 76, 11–23 (2013)CrossRefGoogle Scholar
  12. 12.
    Orabona, F., Luo, J., Caputo, B.: Multi kernel learning with online-batch optimization. J. Mach. Learn. Res. 13, 227–253 (2012)zbMATHMathSciNetGoogle Scholar
  13. 13.
    Shalev-Shwartz, S., Srebro, N.: SVM optimization: inverse dependence on training set size. In: ICML, pp. 928–935 (2008)Google Scholar
  14. 14.
    Jie, L., Orabona, F., Fornoni, M., Caputo, B., Cesa-Bianchi, N.: OM-2: An online multi-class multi-kernel learning algorithm. In: CVPRW, pp. 43–50 (2010)Google Scholar
  15. 15.
    Yang, M., Zhang, L., Feng, X., Zhang, D.: Fisher discrimination dictionary learning for sparse representation. In: ICCV, pp. 543–550 (2011)Google Scholar
  16. 16.
    Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online dictionary learning for sparse coding. In: ICML, p. 87 (2009)Google Scholar
  17. 17.
    Liu, J., Ji, S., Ye, J.: SLEP: Sparse Learning with Efficient Projections. Arizona State University (2009)Google Scholar
  18. 18.
    Wang, L., Shi, F., Lin, W., Gilmore, J.H., Shen, D.: Automatic segmentation of neonatal images using convex optimization and coupled level sets. NeuroImage 58(3), 805–817 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Xuchu Wang
    • 1
    • 2
  • Li Wang
    • 2
  • Heung-Il Suk
    • 2
  • Dinggang Shen
    • 2
  1. 1.Key Laboratory of Optoelectronic Technology and Systems of Ministry of Education, College of Optoelectronic EngineeringChongqing UniversityChongqingChina
  2. 2.Department of Radiology and Biomedical Research Imaging Center (BRIC)University of North Carolina at Chapel HillChapel HillUSA

Personalised recommendations