Accurate and Consistent 4D Segmentation of Serial Infant Brain MR Images

  • Li Wang
  • Feng Shi
  • Pew-Thian Yap
  • John H. Gilmore
  • Weili Lin
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7012)

Abstract

Accurate and consistent segmentation of infant brain MR images plays an important role in quantifying the early brain development, especially in longitudinal studies. However, due to rapid maturation and myelination of brain tissues in the first year of life, white-gray matter contrast undergoes dramatic changes. In fact, the contrast inverses around 6 months of age, where the white and gray matter tissues are isointense and hence exhibit the lowest contrast, posing significant challenges for segmentation algorithms. In this paper, we propose a novel longitudinally guided level set method for segmentation of serial infant brain MR images, acquired from 2 weeks up to 1.5 years of age. The proposed method makes optimal use of T1, T2 and the diffusion weighted images for complimentary tissue distribution information to address the difficulty caused by the low contrast. A longitudinally consistent term, which constrains the distance across the serial images within a biologically reasonable range, is employed to obtain temporally consistent segmentation results. The proposed method has been applied on 22 longitudinal infant subjects with promising results.

Keywords

Gray Matter Fractional Anisotropy Active Contour Tissue 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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Weisenfeld, N.I., Warfield, S.K.: Automatic segmentation of newborn brain MRI. NeuroImage 47(2), 564–572 (2009)CrossRefGoogle Scholar
  2. 2.
    Dietrich, R., et al.: MR evaluation of early myelination patterns in normal and developmentally delayed infants. AJR Am. J. Roentgenol. 150, 889–896 (1988)CrossRefGoogle Scholar
  3. 3.
    Shi, F., et al.: Neonatal brain image segmentation in longitudinal MRI studies. NeuroImage 49(1), 391–400 (2010)CrossRefGoogle Scholar
  4. 4.
    Armstrong, E., et al.: The ontogeny of human gyrification. Cerebral Cortex 5(1), 56–63 (1995)CrossRefGoogle Scholar
  5. 5.
    Wang, L., et al.: Automatic segmentation of neonatal images using convex optimization and coupled level set method. In: Pan, P.J., Fan, X., Yang, Y. (eds.) MIAR 2010. LNCS, vol. 6326, pp. 1–10. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Li, C.: Active contours with local binary fitting energy. In: IMA Workshop on New Mathematics and Algorithms for 3-D Image Analysis (January 2006)Google Scholar
  7. 7.
    Li, C., et al.: Implicit active contours driven by local binary fitting energy. In: CVPR, pp. 1–7 (2007)Google Scholar
  8. 8.
    Li, C., et al.: A variational level set approach to segmentation and bias correction of images with intensity inhomogeneity. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part II. LNCS, vol. 5242, pp. 1083–1091. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Zeng, X., et al.: Segmentation and measurement of the cortex from 3D MR images using coupled surfaces propagation. IEEE TMI 18(10), 100–111 (1999)Google Scholar
  10. 10.
    Shen, D., Davatzikos, C.: Measuring temporal morphological changes robustly in brain MR images via 4-dimensional template warping. NeuroImage 21(4), 1508–1517 (2004)CrossRefGoogle Scholar
  11. 11.
    Shattuck, D., Leahy, R.: Automated graph-based analysis and correction of cortical volume topology. IEEE TMI 20(11), 1167–1177 (2001)Google Scholar
  12. 12.
    Sled, J., Zijdenbos, A., Evans, A.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE TMI 17(1), 87–97 (1998)Google Scholar
  13. 13.
    Chan, T., Vese, L.: Active contours without edges. IEEE TIP 10(2), 266–277 (2001)MATHGoogle Scholar
  14. 14.
    Sethian, J.: Level Set Methods and Fast Marching Methods. Cambridge University Press, Cambridge (1999)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Li Wang
    • 1
  • Feng Shi
    • 1
  • Pew-Thian Yap
    • 1
  • John H. Gilmore
    • 2
  • Weili Lin
    • 3
  • Dinggang Shen
    • 1
  1. 1.Department of Radiology and BRICIDEA LabUSA
  2. 2.Department of PsychiatryUniversity of North Carolina at Chapel HillUSA
  3. 3.MRI Lab, Department of Radiology and BRICUniversity of North Carolina at Chapel HillUSA

Personalised recommendations