Advertisement

Integration of Sparse Multi-modality Representation and Geometrical Constraint for Isointense Infant Brain Segmentation

  • Li Wang
  • Feng Shi
  • Gang Li
  • Weili Lin
  • John H. Gilmore
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8149)

Abstract

Segmentation of infant brain MR images is challenging due to insufficient image quality, severe partial volume effect, and ongoing maturation and myelination process. During the first year of life, the signal contrast between white matter (WM) and gray matter (GM) in MR images undergoes inverse changes. In particular, the inversion of WM/GM signal contrast appears around 6-8 months of age, where brain tissues appear isointense and hence exhibit extremely low tissue contrast, posing significant challenges for automated segmentation. In this paper, we propose a novel segmentation method to address the above-mentioned challenge based on the sparse representation of the complementary tissue distribution information from T1, T2 and diffusion-weighted images. Specifically, we first derive an initial segmentation from a library of aligned multi-modality images with ground-truth segmentations by using sparse representation in a patch-based fashion. The segmentation is further refined by the integration of the geometrical constraint information. The proposed method was evaluated on 22 6-month-old training subjects using leave-one-out cross-validation, as well as 10 additional infant testing subjects, showing superior results in comparison to other state-of-the-art methods.

Keywords

Fractional Anisotropy Sparse Representation Geometrical Constraint Initial Segmentation Fractional Anisotropy Image 
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.
    Weisenfeld, N.I., Warfield, S.K.: Automatic segmentation of newborn brain MRI. Neuroimage 47, 564–572 (2009)CrossRefGoogle Scholar
  2. 2.
    Xue, H., Srinivasan, L., Jiang, S., Rutherford, M., et al.: Automatic segmentation and reconstruction of the cortex from neonatal MRI. Neuroimage (2007)Google Scholar
  3. 3.
    Gui, L., Lisowski, R., Faundez, T., Hüppi, P.S., et al.: Morphology-driven automatic segmentation of MR images of the neonatal brain. Med. Image. Anal. 16, 1565–1579 (2012)CrossRefGoogle Scholar
  4. 4.
    Paus, T., Collins, D.L., Evans, A.C., Leonard, G., et al.: Maturation of white matter in the human brain: a review of magnetic resonance studies. Brain Research Bulletin 54, 255–266 (2001)CrossRefGoogle Scholar
  5. 5.
    Prastawa, M., Gilmore, J.H., Lin, W., Gerig, G.: Automatic segmentation of MR images of the developing newborn brain. Med. Image Anal. 9, 457–466 (2005)CrossRefGoogle Scholar
  6. 6.
    Warfield, S.K., Kaus, M., Jolesz, F.A., Kikinis, R.: Adaptive, template moderated, spatially varying statistical classification. Med. Image Anal. 4, 43–55 (2000)CrossRefGoogle Scholar
  7. 7.
    Shi, F., Yap, P.-T., Gilmore, J.H., Lin, W., et al.: Spatial-temporal constraint for segmentation of serial infant brain MR images. MIAR (2010)Google Scholar
  8. 8.
    Wang, L., Shi, F., Yap, P.-T., Gilmore, J.H., et al.: 4D Multi-Modality Tissue Segmentation of Serial Infant Images. PLoS ONE 7, e44596 (2012)Google Scholar
  9. 9.
    Kim, S.H., Fonov, V.S., Dietrich, C., Vachet, C., et al.: Adaptive prior probability and spatial temporal intensity change estimation for segmentation of the one-year-old human brain. Journal of Neuroscience Methods 212, 43–55 (2013)CrossRefGoogle Scholar
  10. 10.
    Segonne, F., Pacheco, J., Fischl, B.: Geometrically Accurate Topology-Correction of Cortical Surfaces Using Nonseparating Loops. IEEE Trans. Med. Imaging 26, 518–529 (2007)CrossRefGoogle Scholar
  11. 11.
    Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., et al.: Robust Face Recognition via Sparse Representation. IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009)CrossRefGoogle Scholar
  12. 12.
    Tong, T., Wolz, R., Hajnal, J.V., Rueckert, D.: Segmentation of brain MR images via sparse patch representation. In: MICCAI Workshop on Sparsity Techniques in Medical Imaging (STMI) (2012)Google Scholar
  13. 13.
    Liu, T., Li, H., Wong, K., Tarokh, A., et al.: Brain tissue segmentation based on DTI data. Neuroimage 38, 114–123 (2007)CrossRefGoogle Scholar
  14. 14.
    Coupé, P., Manjón, J., Fonov, V., Pruessner, J., et al.: Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. Neuroimage 54, 940–954 (2011)CrossRefGoogle Scholar
  15. 15.
    Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: Efficient non-parametric image registration. Neuroimage 45, S61–S72 (2009)Google Scholar
  16. 16.
    Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: CVPR, pp. 1794–1801 (2009)Google Scholar
  17. 17.
    Wang, J., Yang, J., Yu, K., Lv, F., et al.: Locality-constrained Linear Coding for image classification. In: CVPR, pp. 3360–3367 (2010)Google Scholar
  18. 18.
    Zou, H., Hastie, T.: Regularization and variable selection via the Elastic Net. Journal of the Royal Statistical Society, Series B 67, 301–320 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Wang, L., Shi, F., Lin, W., Gilmore, J.H., et al.: Automatic segmentation of neonatal images using convex optimization and coupled level sets. Neuroimage 58, 805–817 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Li Wang
    • 1
  • Feng Shi
    • 1
  • Gang Li
    • 1
  • Weili Lin
    • 1
  • John H. Gilmore
    • 2
  • Dinggang Shen
    • 1
  1. 1.Department of Radiology and BRICUniversity of North Carolina at Chapel HillUSA
  2. 2.Department of PsychiatryUniversity of North Carolina at Chapel HillUSA

Personalised recommendations