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Topological Correction of Infant Cortical Surfaces Using Anatomically Constrained U-Net

  • Liang Sun
  • Daoqiang Zhang
  • Li Wang
  • Wei Shao
  • Zengsi Chen
  • Weili Lin
  • Dinggang Shen
  • Gang Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11046)

Abstract

Reconstruction of accurate cortical surfaces with minimal topological errors (i.e., handles and holes) from infant brain MR images is important in early brain development studies. However, infant brain MR images usually exhibit extremely low tissue contrast (especially from 3 to 9 months of age) and dynamic imaging appearance patterns. Thus, it is inevitable to have large amounts of topological errors in the infant brain tissue segmentation results, thus leading to inaccurate surface reconstruction. To address these issues, inspired by recent advances in deep learning methods, we propose an anatomically constrained U-Net method for topological correction of infant cortical surfaces. Specifically, in our method, we first extract candidate voxels with potential topological errors, by leveraging a topology-preserving level set method. Then, we propose a U-Net with anatomical constraints to correct those located candidate voxels. Due to the fact that infant cortical surfaces often contain large handles or holes, it is difficult to completely correct all errors using one-shot correction. Therefore, we further gather these two steps into an iterative framework to correct large topological errors gradually. To our knowledge, this is the first work introducing deep learning for infant cortical topological correction. We compare our method with the state-of-the-art method on infant cortical topology and show the superior performance of our method.

References

  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 (2012)CrossRefGoogle Scholar
  2. 2.
    Paus, T., Collins, D., Evans, A., Leonard, G., Pike, B., Zijdenbos, A.: 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.
    Shattuck, D.W., Leahy, R.M.: Automated graph-based analysis and correction of cortical volume topology. IEEE TMI 20(11), 1167–1177 (2001)Google Scholar
  4. 4.
    Fischl, B., Liu, A., Dale, A.M.: Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex. IEEE TMI 20(1), 70–80 (2001)Google Scholar
  5. 5.
    Yotter, R.A., Dahnke, R., Thompson, P.M., Gaser, C.: Topological correction of brain surface meshes using spherical harmonics. Hum. Brain Mapp. 32(7), 1109–1124 (2011)CrossRefGoogle 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. IEEE TMI 32(3), 511–530 (2013)Google Scholar
  7. 7.
    Ségonne, F., Grimson, E., Fischl, B.: A genetic algorithm for the topology correction of cortical surfaces. In: Biennial International Conference on Information Processing in Medical Imaging, Springer (2005) 393–405Google Scholar
  8. 8.
    Hao, S., Li, G., Wang, L., Meng, Y., Shen, D.: Learning-based topological correction for infant cortical surfaces. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9900, pp. 219–227. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46720-7_26CrossRefGoogle Scholar
  9. 9.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar
  10. 10.
    Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46723-8_49CrossRefGoogle Scholar
  11. 11.
    Han, X., Xu, C., Prince, J.L.: A topology preserving level set method for geometric deformable models. IEEE TPAMI 25(6), 755–768 (2003)CrossRefGoogle Scholar
  12. 12.
    Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: efficient non-parametric image registration. NeuroImage 45(1), S61–S72 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Computer Science and Technology, Nanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA
  3. 3.College of Sciences, China Jiliang UniversityHangzhouChina

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