Skip to main content

Brain Cortical Surface Registration with Anatomical Atlas Constraints

  • Conference paper
  • First Online:
Advances in Visual Computing (ISVC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14361))

Included in the following conference series:

  • 447 Accesses

Abstract

This work presents a novel cortical surface registration framework by using the whole anatomical atlas structures as correspondence constraints, which are extracted as atlas graphs (nodes are the junctions and edges are the intersecting curves of regions). The focus of this work is on the geometric registration category of cortical surfaces, i.e., brains are registered only using structural information without any functional information. We aim to innovate the geometric registration framework by utilizing the prominent anatomical features, atlas, to drive the registration. Intuitively, we convert the 3D cortical surfaces to 2D disks by special geometric mappings, where the curvy atlas regions become straight and convex polygonal regions; then registration is achieved between 2D domains such that curvy constrains become linear constraints and are solvable in linear time. The mappings generated are intrinsic and have theoretic guarantee of existence, uniqueness and optimality in terms of constrained harmonic energy. It differs from the literature geometric approaches using brain curves or point features. To the best of our knowledge, it is the first work of using atlas graph constraints in geometric registration. Our experiments on various brain data sets demonstrate the efficiency and efficacy for brain registration and the practicability of the proposed framework for brain disease classification.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Avants, B.B., Tustison, N., Song, G., et al.: Advanced normalization tools (ANTs). Insight J. 2(365), 1–35 (2009)

    Google Scholar 

  2. Che, T., et al.: AMNet: adaptive multi-level network for deformable registration of 3D brain MR images. Med. Image Anal. 85, 102740 (2023)

    Article  Google Scholar 

  3. Cheng, J., Dalca, A.V., Fischl, B., Zöllei, L., Initiative, A.D.N., et al.: Cortical surface registration using unsupervised learning. Neuroimage 221, 117161 (2020)

    Article  Google Scholar 

  4. Choi, P.T., Lam, K.C., Lui, L.M.: FLASH: fast landmark aligned spherical harmonic parameterization for genus-0 closed brain surfaces. SIAM J. Imaging Sci. 8(1), 67–94 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  5. Fischl, B., Sereno, M., Dale, A.: Cortical surface-based analysis II: inflation, flattening, and a surface-based coordinate system. Neuroimage 9(2), 195–207 (1999)

    Article  Google Scholar 

  6. Floater, M.S.: Mean value coordinates. Comput. Aided Geom. Design 20(1), 19–27 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  7. Floater, M.S.: One-to-one piecewise linear mappings over triangulations. Math. Comput. 72(242), 685–696 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  8. Gaser, C., Dahnke, R., Thompson, P.M., Kurth, F., Luders, E., Initiative, A.D.N.: CAT-a computational anatomy toolbox for the analysis of structural MRI data. biorxiv, pp. 2022–06 (2022)

    Google Scholar 

  9. Jack, C.R., et al.: The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27(4), 685–691 (2008)

    Article  Google Scholar 

  10. Klein, A., Tourville, J.: 101 labeled brain images and a consistent human cortical labeling protocol. Front. Brain Imaging Methods 6(171) (2012)

    Google Scholar 

  11. Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97(1–2), 273–324 (1997)

    Article  MATH  Google Scholar 

  12. Kurtek, S., Srivastava, A., Klassen, E., Laga, H.: Landmark-guided elastic shape analysis of spherically-parameterized surfaces. Comput. Graphics Forum 32(2), 429–438 (2013)

    Article  Google Scholar 

  13. Pienaar, R., Fischl, B., Caviness, V., Makris, N., Grant, P.E.: A methodology for analyzing curvature in the developing brain from preterm to adult. Int. J. Imaging Syst. Technol. 18(1), 42–68 (2008)

    Article  Google Scholar 

  14. Razib, M., Lu, Z.L., Zeng, W.: Structural brain mapping. In: International Conference on Medical Image Computing and Computer Assisted Intervention (2015)

    Google Scholar 

  15. Reuter, M., Rosas, H., Fischl, B.: Highly accurate inverse consistent registration: a robust approach. Neuroimage 53(4), 1181–1196 (2010)

    Article  Google Scholar 

  16. Robinson, E.C., et al.: MSM: a new flexible framework for multimodal surface matching. Neuroimage 100, 414–426 (2014)

    Article  Google Scholar 

  17. Schoen, R., Yau, S.T.: Lectures on Harmonic Maps. International Press (1997)

    Google Scholar 

  18. Shattuck, D.W., et al.: Construction of a 3D probabilistic atlas of human cortical structures. NeuroImage 39, 1064–1080 (2007)

    Article  Google Scholar 

  19. Shi, R., et al.: Hyperbolic harmonic brain surface registration with curvature-based landmark matching. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 159–170. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38868-2_14

    Chapter  Google Scholar 

  20. Smith, J., Schaefer, S.: Bijective parameterization with free boundaries. ACM Trans. Graphics 34(4CD), 70.1-70.9 (2015)

    Google Scholar 

  21. Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Advances in Neural Information Processing Systems, pp. 2951–2959 (2012)

    Google Scholar 

  22. Tsui, A., et al.: Globally optimal cortical surface matching with exact landmark correspondence. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 487–498. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38868-2_41

    Chapter  Google Scholar 

  23. Yeo, B.T., Sabuncu, M.R., Vercauteren, T., Ayache, N., Fischl, B., Golland, P.: Spherical demons: fast diffeomorphic landmark-free surface registration. IEEE Trans. Med. Imaging 29(3), 650–668 (2010)

    Article  Google Scholar 

  24. Zeng, W., Yang, Y.-J.: Surface matching and registration by landmark curve-driven canonical quasiconformal mapping. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 710–724. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_46

    Chapter  Google Scholar 

Download references

Acknowledgment

This work was supported by National Key R &D Program of China (Grant No. 2021YFA1003002) and NSFC (Grant No. 12090021). Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wei Zeng .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zeng, W., Chang, X., Yang, L., Razib, M., Lu, ZL., Yang, YJ. (2023). Brain Cortical Surface Registration with Anatomical Atlas Constraints. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2023. Lecture Notes in Computer Science, vol 14361. Springer, Cham. https://doi.org/10.1007/978-3-031-47969-4_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-47969-4_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47968-7

  • Online ISBN: 978-3-031-47969-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics