Abstract
Registration and delineation of anatomical features in MRI of the human brain play an important role in the investigation of brain development and disease. Accurate, automatic and computationally efficient cortical surface registration and delineation of surface-based landmarks, including regions of interest (ROIs) and sulcal curves (sulci), remain challenging problems due to substantial variation in the shapes of these features across populations. We present a method that performs a fast and accurate registration, labeling and sulcal delineation of brain images. The new method presented in this paper uses a multiresolution, curvature based approach to perform a registration of a subject brain surface model to a delineated atlas surface model; the atlas ROIs and sulcal curves are then mapped to the subject brain surface. A geodesic curvature flow on the cortical surface is then used to refine the locations of the sulcal curves sulci and label boundaries further, such that they follow the true sulcal fundi more closely. The flow is formulated using a level set based method on the cortical surface, which represents the curves as zero level sets. We also incorporate a curvature based weighting that drives the curves to the bottoms of the sulcal valleys in the cortical folds. Finally, we validate our new approach by comparing sets of automatically delineated sulcal curves it produced to corresponding sets of manually delineated sulcal curves. Our results indicate that the proposed method is able to find these landmarks accurately.
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References
Cheng, L., Burchard, P., Merriman, B., Osher, S.: Motion of curves constrained on surfaces using a level-set approach. Journal of Computational Physics 175(2), 604–644 (2002)
Fischl, B., Sereno, M.I., Tootell, R.B.H., Dale, A.M.: High-resolution inter-subject averaging and a coordinate system for the cortical surface. Human Brain Mapping 8, 272–284 (1998)
Joshi, A., Shattuck, D., Damasio, H., Leahy, R.: Geodesic curvature flow on surfaces for automatic sulcal delineation. In: Proc. ISBI (2012)
Joshi, A.A., Shattuck, D.W., Thompson, P.M., Leahy, R.M.: Surface-constrained volumetric brain registration using harmonic mappings. IEEE Trans. Med. Imag. 26(12), 1657–1669 (2007)
Joshi, A., Chaudhari, A., Li, C., Dutta, J., Cherry, S., Shattuck, D., Toga, A., Leahy, R.: Digiwarp: a method for deformable mouse atlas warping to surface topographic data. Physics in Medicine and Biology 55, 6197 (2010)
Lai, R., Shi, Y., Sicotte, N., Toga, A.W.: Automated corpus callosum extraction via laplace-beltrami nodal parcellation and intrinsic geodesic curvature flows on surfaces. In: ICCV (2011)
Liu, D., Nocedal, J.: On the limited memory bfgs method for large scale optimization. Mathematical Programming 45(1), 503–528 (1989)
Narr, K., Thompson, P., Sharma, T., Moussai, J., Zoumalan, C., Rayman, J., Toga, A.: Three-dimensional mapping of gyral shape and cortical surface asymmetries in schizophrenia: gender effects. Am. J. Psychiatry 158(2), 244–255 (2001)
Pantazis, D., Joshi, A., Jiang, J., Shattuck, D., Bernstein, L., Damasio, H., Leahy, R.: Comparison of landmark-based and automatic methods for cortical surface registration. Neuroimage 49(3), 2479–2493 (2010)
Rettmann, M., Kraut, M., Prince, J., Resnick, S.: Cross-sectional and longitudinal analyses of anatomical sulcal changes associated with aging. Cerebral Cortex 16(11), 1584–1594 (2006)
Sadiku, M.N.O.: Numerical techniques in electromagnetics. CRC (2000)
Shattuck, D.W., Leahy, R.M.: Brainsuite: An automated cortical surface identification tool. Medical Image Analysis 8(2), 129–142 (2002)
Shattuck, D., Joshi, A., Pantazis, D., Kan, E., Dutton, R., Sowell, E., Thompson, P., Toga, A., Leahy, R.: Semi-automated method for delineation of landmarks on models of the cerebral cortex. J. Neuroscience Meth. 178(2), 385–392 (2009)
Spira, A., Kimmel, R.: Geodesic curvature flow on parametric surfaces. In: Curve and Surface Design, pp. 365–373 (2002)
Tao, X., Prince, J., Davatzikos, C.: Using a statistical shape model to extract sulcal curves on the outer cortex of the human brain. IEEE Transactions on Medical Imaging 21(5), 513–524 (2002)
Tosun, D., Prince, J.L.: Cortical Surface Alignment Using Geometry Driven Multispectral Optical Flow. In: Christensen, G.E., Sonka, M. (eds.) IPMI 2005. LNCS, vol. 3565, pp. 480–492. Springer, Heidelberg (2005)
Vaillant, M., Davatzikos, C.: Finding parametric representations of the cortical sulci using an active contour model. Medical Image Analysis 1(4), 295–315 (1997)
Woods, R., Grafton, S., Holmes, C., Cherry, S., Mazziotta, J.: Automated image registration: I. general methods and intrasubject, intramodality validation. Journal of Computer Assisted Tomography 22(1), 139 (1998)
Wu, C., Tai, X.: A level set formulation of geodesic curvature flow on simplicial surfaces. IEEE Transactions on Visualization and Computer Graphics 16(4), 647–662 (2010)
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Joshi, A.A., Shattuck, D.W., Leahy, R.M. (2012). A Method for Automated Cortical Surface Registration and Labeling. In: Dawant, B.M., Christensen, G.E., Fitzpatrick, J.M., Rueckert, D. (eds) Biomedical Image Registration. WBIR 2012. Lecture Notes in Computer Science, vol 7359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31340-0_19
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DOI: https://doi.org/10.1007/978-3-642-31340-0_19
Publisher Name: Springer, Berlin, Heidelberg
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