Abstract
In this paper we propose a novel system for the accurate reconstruction of cortical surfaces from magnetic resonance images. At the core of our system is a novel framework for outlier detection and pruning by integrating intrinsic Reeb analysis of Laplace-Beltrami eigen-functions with topology-preserving evolution for localized filtering of outliers, which avoids unnecessary smoothing and shrinkage of cortical regions with high curvature. In our experiments, we compare our method with FreeSurfer and illustrate that our results can better capture cortical geometry in deep sulcal regions. To demonstrate the robustness of our method, we apply it to over 1300 scans from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We show that cross-sectional group differences and longitudinal changes can be detected successfully with our method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Med. Image. Anal. 12(1), 26–42 (2008)
Dale, A.M., Fischl, B., Sereno, M.I.: Cortical surface-based analysis i: segmentation and surface reconstruction. NeuroImage 9, 179–194 (1999)
Dubois, J., Benders, M., Borradori-Tolsa, C., et al.: Primary cortical folding in the human newborn: an early marker of later functional development. Brain 131(8), 2028–2041 (2008)
Han, X., Pham, D.L., Tosun, D., et al.: CRUISE: Cortical reconstruction using implicit surface evolution. NeuroImage 23, 997–1012 (2004)
Han, X., Xu, C., Prince, J.: A topology preserving level set method for geometric deformable models. IEEE Trans. Pattern Anal. Machine Intell. 25(6), 755–768 (2003)
Kim, J.S., Singh, V., Lee, J.K., et al.: Automated 3-d extraction and evaluation of the inner and outer cortical surfaces using a laplacian map and partial volume effect classification. NeuroImage 27, 210–221 (2005)
Latecki, L.J.: 3D well-composed pictures. Graph. Models Image Process. 59(3), 164–172 (1997)
Leung, K., Parker, D., Cunha, A., et al.: IRMA: An image registration meta-algorithm evaluating alternative algorithms with multiple metrics. In: Proc. Int. Conf. on Scientific and Statistical Data Base Management, pp. 612–617 (2008)
Mangin, J.F., Frouin, V., Bloch, I., et al.: From 3D magnetic resonance images to structural representations of the cortex topography using topology preserving deformations. Journal of Mathematical Imaging and Vision 5(4), 297–318 (1995)
Mazziotta, J.C., Toga, A.W., Evans, A.C., et al.: A probabilistic atlas and reference system for the human brain: international consortium for brain mapping. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 356, 1293–1322 (2001)
Mueller, S., Weiner, M., Thal, L., et al.: The Alzheimer’s disease neuroimaging initiative. Clin. North Am. 15, 869–877 (2005)
Niethammer, M., Reuter, M., Wolter, F.-E., Bouix, S., Peinecke, N., Koo, M.-S., Shenton, M.E.: Global medical shape analysis using the laplace-beltrami spectrum. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 850–857. Springer, Heidelberg (2007)
Patenaude, B.: Bayesian Statistical Models of Shape and Appearance for Subcortical Brain Segmentation. Ph.D. thesis, University of Oxford (2007)
Qiu, A., Bitouk, D., Miller, M.I.: Smooth functional and structural maps on the neocortex via orthonormal bases of the Laplace-Beltrami operator. IEEE Trans. Med. Imag. 25(10), 1296–1306 (2006)
Reuter, M.: Hierarchical shape segmentation and registration via topological features of Laplace-Beltrami eigenfunctions. Int’l Journal of Computer Vision 89(2-3), 287–308 (2010)
Shattuck, D., Leahy, R.: BrainSuite: An automated cortical surface identification tool. Med. Image. Anal. 8(2), 129–142 (2002)
Shattuck, D., Mirza, M., Adisetiyo, V., et al.: Construction of a 3d probabilistic atlas of human brain structures. NeuroImage 39(3), 1064–1080 (2008)
Shi, Y., Karl, W.C.: A real-time algorithm for the approximation of level-set-based curve evolution. IEEE Trans. Image Processing 17(5), 645–657 (2008)
Shi, Y., Lai, R., Krishna, S., et al.: Anisotropic Laplace-Beltrami eigenmaps: Bridging Reeb graphs and skeletons. In: Proc. MMBIA, pp. 1–7 (2008)
Shi, Y., Lai, R., Morra, J., et al.: Robust surface reconstruction via laplace-beltrami eigen-projection and boundary deformation. IEEE Trans. Med. Imag. 29(12), 2009–2022 (2010)
Shi, Y., Sun, B., Lai, R., Dinov, I., Toga, A.W.: Automated sulci identification via intrinsic modeling of cortical anatomy. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6363, pp. 49–56. Springer, Heidelberg (2010)
Siddiqi, K., Bouix, S., Tannebaum, A., Zuker, S.: Hamilton-Jacobi skeletons. Int’l Journal of Computer Vision 48(3), 215–231 (2002)
Sled, J., Zijdenbos, A., Evans, A.: A nonparametric method for automatic correction of intensity nonuniformity in mri data. IEEE Trans. Med. Imag. 17(1), 87–97 (1998)
Thompson, P.M., Hayashi, K.M., Sowell, E.R., et al.: Mapping cortical change in alzheimer’s disease, brain development, and schizophrenia. NeuroImage 23, S2–S18 (2004)
Uhlenbeck, K.: Generic properties of eigenfunctions. Amer. J. of Math. 98(4), 1059–1078 (1976)
Van Essen, D.C., Drury, H.A., Joshi, S., Miller, M.I.: Functional and structural mapping of human cerebral cortex: solutions are in the surfaces. Proc. Natl. Acad. Sci. USA 95, 788–795 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shi, Y., Lai, R., Toga, A.W. (2011). CoRPORATE: Cortical Reconstruction by Pruning Outliers with Reeb Analysis and Topology-Preserving Evolution. In: Székely, G., Hahn, H.K. (eds) Information Processing in Medical Imaging. IPMI 2011. Lecture Notes in Computer Science, vol 6801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22092-0_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-22092-0_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22091-3
Online ISBN: 978-3-642-22092-0
eBook Packages: Computer ScienceComputer Science (R0)