Learning-Based Topological Correction for Infant Cortical Surfaces

  • Shijie Hao
  • Gang Li
  • Li Wang
  • Yu Meng
  • Dinggang ShenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9900)


Reconstruction of topologically correct and accurate cortical surfaces from infant MR images is of great importance in neuroimaging mapping of early brain development. However, due to rapid growth and ongoing myelination, infant MR images exhibit extremely low tissue contrast and dynamic appearance patterns, thus leading to much more topological errors (holes and handles) in the cortical surfaces derived from tissue segmentation results, in comparison to adult MR images which typically have good tissue contrast. Existing methods for topological correction either rely on the minimal correction criteria, or ad hoc rules based on image intensity priori, thus often resulting in erroneous correction and large anatomical errors in reconstructed infant cortical surfaces. To address these issues, we propose to correct topological errors by learning information from the anatomical references, i.e., manually corrected images. Specifically, in our method, we first locate candidate voxels of topologically defected regions by using a topology-preserving level set method. Then, by leveraging rich information of the corresponding patches from reference images, we build region-specific dictionaries from the anatomical references and infer the correct labels of candidate voxels using sparse representation. Notably, we further integrate these two steps into an iterative framework to enable gradual correction of large topological errors, which are frequently occurred in infant images and cannot be completely corrected using one-shot sparse representation. Extensive experiments on infant cortical surfaces demonstrate that our method not only effectively corrects the topological defects, but also leads to better anatomical consistency, compared to the state-of-the-art methods.


Sparse Representation Cortical Surface Topological Defect Anatomical Reference Tissue Segmentation 
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.



This work was supported in part by NIH grants (MH107815, MH108914, MH100217, EB006733, EB008374, and EB009634). Dr. Shijie Hao was supported by National Nature Science Foundation of China grant 61301222.


  1. 1.
    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
  2. 2.
    Paus, T., et al.: Maturation of white matter in the human brain: a review of magnetic resonance studies. Brain Res. Bull. 54(3), 255–266 (2001)CrossRefGoogle Scholar
  3. 3.
    Wang, L., et al.: LINKS: learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images. NeuroImage 108, 160–172 (2015)CrossRefGoogle Scholar
  4. 4.
    Shattuck, D.W., Leahy, R.M.: Automated graph-based analysis and correction of cortical volume topology. TMI 20(11), 1167–1177 (2001)Google Scholar
  5. 5.
    Han, X., et al.: Topology correction in brain cortex segmentation using a multiscale, graph-based algorithm. TMI 21(2), 109–121 (2002)Google Scholar
  6. 6.
    Shi, Y., Lai, R., Toga, A.W.: Cortical surface reconstruction via unified Reeb analysis of geometric and topological outliers in magnetic resonance images. TMI 32(3), 511–530 (2013)Google Scholar
  7. 7.
    Fischl, B., et al.: Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex. TMI 20(1), 70–80 (2001)Google Scholar
  8. 8.
    Segonne, F., Pacheco, J., Fischl, B.: Geometrically accurate topology-correction of cortical surfaces using nonseparating loops. TMI 26(4), 518–529 (2007)Google Scholar
  9. 9.
    Yotter, R.A., et al.: Topological correction of brain surface meshes using spherical harmonics. Hum. Brain Mapp. 32(7), 1109–1124 (2011)CrossRefGoogle Scholar
  10. 10.
    Bazin, P.-L., Pham, D.: Topology correction of segmented medical images using a fast marching algorithm. Comput. Methods Prog. Biomed. 88(2), 182–190 (2007)CrossRefGoogle Scholar
  11. 11.
    Ségonne, F., Grimson, W.L., Fischl, B.: A genetic algorithm for the topology correction of cortical surfaces. In: Christensen, G.E., Sonka, M. (eds.) IPMI 2005. LNCS, vol. 3565, pp. 393–405. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Han, X., Xu, C., Prince, J.: A topology preserving level set method for geometric deformable models. PAMI 25(6), 755–768 (2003)CrossRefGoogle Scholar
  13. 13.
    Vercauteren, T., et al.: Diffeomorphic demons: efficient non-parametric image registration. NeuroImage 45(S1), 61–72 (2009)MathSciNetCrossRefGoogle 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)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Shijie Hao
    • 1
    • 2
  • Gang Li
    • 2
  • Li Wang
    • 2
  • Yu Meng
    • 2
  • Dinggang Shen
    • 2
    Email author
  1. 1.School of Computer and InformationHefei University of TechnologyAnhuiChina
  2. 2.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA

Personalised recommendations