Abstract
We describe a new approach to estimating the cortical thickness of human brains using magnetic resonance imaging data. Our algorithm is part of a processing chain consisting of a brain segmentation (skull stripping), as well as white and grey matter segmentation procedures. In this paper, only the grey matter segmentation together with the cortical thickness estimation is described. In contrast to many existing methods, our estimation method is voxel-based and does not use any surface meshes. While this fact poses a principal limit on the accuracy that can be achieved by our method, it offers tremendous advantages with respect to practical applicability. In particular, it is applicable to data sets showing severe cortical atrophies that involve areas of high curvature and extremely thin gyral stalks. In contrast to many other methods, it is entirely automatic and very fast with computation times of a few minutes. Our method has been used in two clinical studies involving a total of 27 patients and 23 healthy subjects.
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References
Kabani, N., Le Goualher, G., MacDonald, D., Evans, A.C.: Measurement of cortical thickness using an automated 3-D algorithm: a validation study. Neuroimage 13, 375–380 (2001)
Jones, S.E., Buchbinder, B.R., Aharon, I.: Three-dimensional mapping of cortical thickness using laplace’s equation. Human Brain Mapping 11(1), 12–32 (2000)
Fischl, B., Dale, A.M.: Measuring the thickness of the human cerebral cortex from magnetic resonance images. PNAS 97(20), 11050–11055 (2000)
Brodmann, K.: Vergleichende Lokalisationslehre der Grosshirnrinde in ihren Prinzipien dargestellt auf Grund des Zellaufbaus. Barth, Leipzig, Germany (1909)
Von Economo, C., Koskinas, G.: Die Cytoarchitektonik der Hirnrinde des erwachsenen Menschen. Springer, Berlin (1925)
Thompson, P., Moussai, J., Zohoori, S., Goldkorn, A., Khan, A.A., Mega, M.S., Small, G.W., Cummings, J.L., Toga, A.W.: Cortical variability and asymmetry in normal aging and alzheimer’s disease. Cerebral Cortex 8, 492–509 (1998)
Dale, A.M., Fischl, B., Sereno, M.I.: Cortical surface-based analysis. I. segmentation and surface reconstruction. Neuroimage 9(2), 179–194 (1999)
MacDonald, D., Kabani, N., Evans, A.C.: Automated 3-D extraction of inner and outer surfaces of cerebral cortex from MRI. Neuroimage 12, 34–356 (2000)
Davatzikos, C., Prince, J.L.: An active contour model for mapping the cortex. IEEE Transactions on Medical Imaging 14(1), 65–80 (1995)
Zeng, X.L., Staib, L.H., Schultz, R.T., Duncan, J.S.: Segmentation and measurement of the cortex from 3-D MR images using coupled-surfaces propagation. IEEE Trans. Med. Imaging 18(10), 927–937 (1999)
Goldenberg, R., Kimmel, R., Rivlin, E., Rudzsky, M.: Variational and level set methods in computer vision. In: IEEE Workshop on Variational and Level Set Methods in Computer Vision, Vancouver, Canada (July 2001)
Teo, P.C., Sapiro, G., Wandell, B.A.: Creating connected representations of cortical grey matter for functional MRI visualization. IEEE Transactions on Medical Imaging 16, 852–863 (1997)
Joshi, M., Ciu, J., Doolittle, K., Joshi, S., Essen, D.V., Wang, L., Miller, M.I.: Brain segmentation and the generation of cortical surfaces. Neuroimage 9, 461–476 (1999)
Miller, M.I., Massie, A.B., Ratnanather, J.T., Botteron, K.N., Csernansky, J.G.: Bayesian construction of geometrically based cortical thickness metrics. Neuroimage 12, 676–687 (2000)
Xu, C., Pham, D.L., Rettmann, M.E., Yu, D.N., Prince, J.L.: Reconstruction of the human cerebral cortex from magnetic resonance images. IEEE Trans. Med. Imaging 18(6), 467–480 (1999)
McInerney, T., Terzopoulos, D.: Deformable models in medical image analysis: A survey. Medical Image Analysis 1(2), 91–108 (1996)
Suri, J.S., Liu, K., Singh, S., Laxminarayan, S.N., Zeng, X., Reden, L.: Shape recovery algorithms using level sets in 2D/3D medical imagery: a state of the art review. IEEE trans. on information technology in biomedicine 6(1), 8–28 (2002)
Ugurbil, K., Garwood, M., Ellermann, J., Hendrich, K., Hinke, R., Hu, X., Kim, S.-G., Menon, R., Merkle, H., Ogawa, S., Salmi, R.: Imaging at high magnetic fields: Initial experiences at 4T. Magn. Reson. Quart. 9(259) (1993)
Norris, D.G.: Reduced power multi-slice MDEFT imaging. J. Magn. Reson. Imaging 11, 445–451 (2000)
Lohmann, G., Preul, C., Hund-Georgiadis, M.: Geometry-preserving white matter segmentation using T1-weighted MRI data. In: Human Brain Mapping 2000, Meeting, New York, USA, June 18-22 (2003) (accepted)
Bertrand, G., Malandain, G.: A new characterization of three-dimensional simple points. Pattern Recognition Letters 15, 169–175 (1994)
Tsao, Y.F., Fu, K.S.: A parallel thinning algorithm for 3D pictures. Computer Graphics Image Proc. 17, 315–331 (1981)
Malandain, G., Fernandez-Vidal, S.: Euclidean skeletons. Image and Vision Computing 16, 317–327 (1998)
Borgefors, G., Nyström, I., Sanniti Di Baja, G.: Computing skeletons in three dimensions. Pattern Recognition 32, 1225–1236 (1999)
Manzanera, A., Bernard, T., Preteux, F., Longuet, B.: nD skeletonization: a unified mathematical framework. Journal of Electronic Engineering 11, 25–37 (2002)
Saito, T., Toriwaki, J.-I.: New algorithms for euclidean distance transformation of an n-dimensional digitized picture with applications. Pattern Recognition 27(11), 1551–1565 (1994)
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Lohmann, G., Preul, C., Hund-Georgiadis, M. (2003). Morphology-Based Cortical Thickness Estimation. In: Taylor, C., Noble, J.A. (eds) Information Processing in Medical Imaging. IPMI 2003. Lecture Notes in Computer Science, vol 2732. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45087-0_8
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DOI: https://doi.org/10.1007/978-3-540-45087-0_8
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