4D Infant Cortical Surface Atlas Construction Using Spherical Patch-Based Sparse Representation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)


The 4D infant cortical surface atlas with densely sampled time points is highly needed for neuroimaging analysis of early brain development. In this paper, we build the 4D infant cortical surface atlas firstly covering 6 postnatal years with 11 time points (i.e., 1, 3, 6, 9, 12, 18, 24, 36, 48, 60, and 72 months), based on 339 longitudinal MRI scans from 50 healthy infants. To build the 4D cortical surface atlas, first, we adopt a two-stage groupwise surface registration strategy to ensure both longitudinal consistency and unbiasedness. Second, instead of simply averaging over the co-registered surfaces, a spherical patch-based sparse representation is developed to overcome possible surface registration errors across different subjects. The central idea is that, for each local spherical patch in the atlas space, we build a dictionary, which includes the samples of current local patches and their spatially-neighboring patches of all co-registered surfaces, and then the current local patch in the atlas is sparsely represented using the built dictionary. Compared to the atlas built with the conventional methods, the 4D infant cortical surface atlas constructed by our method preserves more details of cortical folding patterns, thus leading to boosted accuracy in registration of new infant cortical surfaces.


  1. 1.
    Fischl, B., et al.: High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp. 8(4), 272–284 (1999)CrossRefGoogle Scholar
  2. 2.
    Glasser, M.F., et al.: A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016)CrossRefGoogle Scholar
  3. 3.
    Hill, J., et al.: A surface-based analysis of hemispheric asymmetries and folding of cerebral cortex in term-born human infants. J. Neurosci. 30(6), 2268–2276 (2010)CrossRefGoogle Scholar
  4. 4.
    Li, G., et al.: Mapping region-specific longitudinal cortical surface expansion from birth to 2 years of age. Cereb. Cortex 23(11), 2724–2733 (2013)CrossRefGoogle Scholar
  5. 5.
    Li, G., et al.: Measuring the dynamic longitudinal cortex development in infants by reconstruction of temporally consistent cortical surfaces. Neuroimage 90, 266–279 (2014)CrossRefGoogle Scholar
  6. 6.
    Li, G., et al.: Construction of 4d high-definition cortical surface atlases of infants: Methods and applications. Med. Image Anal. 25(1), 22–36 (2015)CrossRefGoogle Scholar
  7. 7.
    Lyttelton, O., et al.: An unbiased iterative group registration template for cortical surface analysis. Neuroimage 34(4), 1535–1544 (2007)CrossRefGoogle Scholar
  8. 8.
    Mairal, J., et al.: Sparse modeling for image and vision processing. Found. Trends Comput. Graph. Vis. 8(2–3), 85–283 (2014)CrossRefGoogle Scholar
  9. 9.
    Tardif, C.L., et al.: Multi-contrast multi-scale surface registration for improved alignment of cortical areas. Neuroimage 111, 107–122 (2015)CrossRefGoogle Scholar
  10. 10.
    Van Essen, D.C., Dierker, D.L.: Surface-based and probabilistic atlases of primate cerebral cortex. Neuron 56(2), 209–225 (2007)CrossRefGoogle Scholar
  11. 11.
    Van Essen, D.C., et al.: The wu-minn human connectome project: an overview. Neuroimage 80, 62–79 (2013)CrossRefGoogle Scholar
  12. 12.
    Wang, L., et al.: Links: learning-based multi-source integration framework for segmentation of infant brain images. Neuroimage 108, 160–172 (2015)CrossRefGoogle Scholar
  13. 13.
    Yeo, B.T., et al.: Spherical demons: fast diffeomorphic landmark-free surface registration. IEEE Trans. Med. Imaging 29(3), 650–668 (2010)CrossRefGoogle Scholar
  14. 14.
    Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. Roy. Stat. Soc. Ser. B 67(2), 301–320 (2005)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Radiology and BRICUNC at Chapel HillChapel HillUSA

Personalised recommendations