Consistent cortical reconstruction and multi-atlas brain segmentation

NeuroImage

Volumn 138, Issue , 2016, Pages 197-210

  Huo, Yuankai ^a   Plassard, Andrew J ^a   Carass, Aaron ^b   Resnick, Susan M ^c   Pham, Dzung L ^d   Prince, Jerry L ^b   Landman, Bennett A ^a,e  

^a VANDERBILT UNIVERSITY   (United States)

^b JOHNS HOPKINS UNIVERSITY   (United States)

^c NATIONAL INSTITUTE ON AGING   (United States)

^d Henry Jackson Foundation   (United States)

^e VANDERBILT UNIVERSITY   (United States)

Author keywords

Cerebral cortex; Cortical reconstruction; Magnetic resonance imaging; Multi atlas segmentation

Indexed keywords

ADULT; AGED; ANATOMIC LANDMARK; ARTICLE; CLINICAL ARTICLE; COMPARATIVE STUDY; COMPUTER PROGRAM; CONCEPTUAL FRAMEWORK; CONTROLLED STUDY; FEMALE; FUZZY SYSTEM; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; IMAGE RECONSTRUCTION; MALE; MEASUREMENT ACCURACY; MULTI ATLAS BRAIN SEGMENTATION; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; SENSITIVITY ANALYSIS; VALIDATION STUDY; VOXEL BASED MORPHOMETRY; WHITE MATTER LESION; ALGORITHM; ANATOMY AND HISTOLOGY; AUTOMATED PATTERN RECOGNITION; BRAIN CORTEX; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC IMAGING; EVALUATION STUDY; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; MIDDLE AGED; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ADULT; ALGORITHMS; CEREBRAL CORTEX; FEMALE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 84969523704     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2016.05.030     Document Type: Article

References (94)

1. Aljabar, P., Heckemann, R.A., Hammers, A., Hajnal, J.V., Rueckert, D., Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy. NeuroImage 46 (2009), 726–738.
2. Artaechevarria, X., Munoz-Barrutia, A., Ortiz-de-Solorzano, C., Combination strategies in multi-atlas image segmentation: application to brain MR data. IEEE Trans. Med. Imaging 28 (2009), 1266–1277.
3. Asman, A.J., Landman, B.A., Formulating spatially varying performance in the statistical fusion framework. IEEE Trans. Med. Imaging 31 (2012), 1326–1336.
4. Asman, A.J., Landman, B.A., Non-local statistical label fusion for multi-atlas segmentation. Med. Image Anal. 17 (2013), 194–208.
5. Asman, A.J., Dagley, A.S., Landman, B.A., Statistical label fusion with hierarchical performance models. Proc. Soc. Photo. Opt. Instrum. Eng., 9034, 2014, 90341E.
6. Asman, A.J., Huo, Y., Plassard, A.J., Landman, B.A., Multi-atlas learner fusion: an efficient segmentation approach for large-scale data. Med. Image Anal. 26 (2015), 82–91.
7. 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 (2008), 26–41.
8. Balafar, M.A., Ramli, A.R., Saripan, M.I., Mashohor, S., Review of brain MRI image segmentation methods. Artif. Intell. Rev. 33 (2010), 261–274.
9. Bazin, P.L., Pham, D.L., Topology-preserving tissue classification of magnetic resonance brain images. IEEE Trans. Med. Imaging 26 (2007), 487–496.
10. Bazin, P.L., Pham, D.L., Homeomorphic brain image segmentation with topological and statistical atlases. Med. Image Anal. 12 (2008), 616–625.
11. Brewer, J.B., Fully-automated volumetric MRI with normative ranges: translation to clinical practice. Behav. Neurol. 21 (2009), 21–28.
12. Brewer, J.B., Magda, S., Airriess, C., Smith, M.E., Fully-automated quantification of regional brain volumes for improved detection of focal atrophy in Alzheimer disease. AJNR Am. J. Neuroradiol. 30 (2009), 578–580.
13. Cabezas, M., Oliver, A., Llado, X., Freixenet, J., Cuadra, M.B., A review of atlas-based segmentation for magnetic resonance brain images. Comput. Methods Prog. Biomed. 104 (2011), e158–e177.
14. Carass, A., Cuzzocreo, J., Wheeler, M.B., Bazin, P.L., Resnick, S.M., Prince, J.L., Simple paradigm for extra-cerebral tissue removal: algorithm and analysis. NeuroImage 56 (2011), 1982–1992.
15. Cardinale, F., Chinnici, G., Bramerio, M., Mai, R., Sartori, I., Cossu, M., Russo, G.L., Castana, L., Colombo, N., Caborni, C., Validation of FreeSurfer-estimated brain cortical thickness: comparison with histologic measurements. Neuroinformatics 12 (2014), 535–542.
16. Chung, M.K., Worsley, K.J., Robbins, S., Paus, T., Taylor, J., Giedd, J.N., Rapoport, J.L., Evans, A.C., Deformation-based surface morphometry applied to gray matter deformation. NeuroImage 18 (2003), 198–213.
17. Cocosco, C.A., Zijdenbos, A.P., Evans, A.C., A fully automatic and robust brain MRI tissue classification method. Med. Image Anal. 7 (2003), 513–527.
18. Cohen, J., Statistical power analysis for the behavioral sciences. 1977, Lawrence Erlbaum Associates, Inc.
19. Cootes, T.F., Edwards, G.J., Taylor, C.J., Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 23 (2001), 681–685.
20. Coupé, P., Manjón, J.V., Fonov, V., Pruessner, J., Robles, M., Collins, D.L., Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. NeuroImage 54 (2011), 940–954.
21. Dale, A.M., Fischl, B., Sereno, M.I., Cortical surface-based analysis. I. Segmentation and surface reconstruction. NeuroImage 9 (1999), 179–194.
22. Desikan, R.S., Segonne, F., Fischl, B., Quinn, B.T., Dickerson, B.C., Blacker, D., Buckner, R.L., Dale, A.M., Maguire, R.P., Hyman, B.T., Albert, M.S., Killiany, R.J., An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31 (2006), 968–980.
23. Destrieux, C., Fischl, B., Dale, A., Halgren, E., Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. NeuroImage 53 (2010), 1–15.
24. Doan, N.T., de Xivry, J.O., Macq, B., Effect of inter-subject variation on the accuracy of atlas-based segmentation applied to human brain structures. SPIE Medical Imaging, 2010, International Society for Optics and Photonics, 76231S–776210.
25. Evans, A.C., Collins, D.L., Mills, S., Brown, E., Kelly, R., Peters, T.M., 3D Statistical Neuroanatomical Models from 305 MRI Volumes. Nuclear Science Symposium and Medical Imaging Conference, 1993. 1993 IEEE Conference Record, 1993, IEEE, 1813–1817.
26. Fan, S.W., Zhou, Z.J., Hu, Z.J., Fang, X.Q., Zhao, F.D., Zhang, J., Quantitative MRI analysis of the surface area, signal intensity and MRI index of the central bright area for the evaluation of early adjacent disc degeneration after lumbar fusion. Eur. Spine J. 21 (2012), 1709–1715.
27. Feczko, E., Augustinack, J.C., Fischl, B., Dickerson, B.C., An MRI-based method for measuring volume, thickness and surface area of entorhinal, perirhinal, and posterior parahippocampal cortex. Neurobiol. Aging 30 (2009), 420–431.
28. Fischl, B., FreeSurfer. NeuroImage 62 (2012), 774–781.
29. Fischl, B., Dale, A.M., Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl. Acad. Sci. U. S. A. 97 (2000), 11050–11055.
30. Fischl, B., Sereno, M.I., Dale, A.M., Cortical surface-based analysis. II: inflation, flattening, and a surface-based coordinate system. NeuroImage 9 (1999), 195–207.
31. Fischl, B., Sereno, M.I., Tootell, R.B.H., Dale, A.M., High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp. 8 (1999), 272–284.
32. Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., Montillo, A., Makris, N., Rosen, B., Dale, A.M., Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33 (2002), 341–355.
33. Fischl, B., Salat, D.H., van der Kouwe, A.J., Makris, N., Segonne, F., Quinn, B.T., Dale, A.M., Sequence-independent segmentation of magnetic resonance images. NeuroImage 23:Suppl. 1 (2004), S69–S84.
34. Fischl, B., van der Kouwe, A., Destrieux, C., Halgren, E., Segonne, F., Salat, D.H., Busa, E., Seidman, L.J., Goldstein, J., Kennedy, D., Caviness, V., Makris, N., Rosen, B., Dale, A.M., Automatically parcellating the human cerebral cortex. Cereb. Cortex 14 (2004), 11–22.
35. Fischl, B., van der Kouwe, A., Destrieux, C., Halgren, E., Segonne, F., Salat, D.H., Busa, E., Seidman, L.J., Goldstein, J., Kennedy, D., Caviness, V., Makris, N., Rosen, B., Dale, A.M., Automatically parcellating the human cerebral cortex. Cereb. Cortex 14 (2004), 11–22.
36. Fornito, A., Yucel, M., Wood, S.J., Adamson, C., Velakoulis, D., Saling, M.M., McGorry, P.D., Panteliss, C., Surface-based morphometry of the anterior cingulate cortex in first episode schizophrenia. Hum. Brain Mapp. 29 (2008), 478–489.
37. Gass, T., Székely, G., Goksel, O., Semi-supervised segmentation using multiple segmentation hypotheses from a single atlas. Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging, 2013, Springer, 29–37.
38. Guimond, A., Meunier, J., Thirion, J.-P., Average brain models: a convergence study. Comput. Vis. Image Underst. 77 (2000), 192–210.
39. Han, X., Fischl, B., Atlas renormalization for improved brain MR image segmentation across scanner platforms. IEEE Trans. Med. Imaging 26 (2007), 479–486.
40. Han, X., Jovicich, J., Salat, D., van der Kouwe, A., Quinn, B., Czanner, S., Busa, E., Pacheco, J., Albert, M., Killiany, R., Maguire, P., Rosas, D., Makris, N., Dale, A., Dickerson, B., Fischl, B., Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer. NeuroImage 32 (2006), 180–194.
41. Han, X., Pham, D.L., Tosun, D., Rettmann, M.E., Xu, C., Prince, J.L., CRUISE: cortical reconstruction using implicit surface evolution. NeuroImage 23 (2004), 997–1012.
42. Han, X., Xu, C., Braga-Neto, U., Prince, J.L., Topology correction in brain cortex segmentation using a multiscale, graph-based algorithm. IEEE Trans. Med. Imaging 21 (2002), 109–121.
43. Han, X., Xu, C., Tosun, D., Prince, J.L., Cortical surface reconstruction using a topology preserving geometric deformable model. Mathematical Methods in Biomedical Image Analysis, 2001. MMBIA 2001. IEEE Workshop on, 2001, IEEE, 213–220.
44. Heckemann, R.A., Hajnal, J.V., Aljabar, P., Rueckert, D., Hammers, A., Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage 33 (2006), 115–126.
45. Huo, Y., Carass, A., Resnick, S.M., Pham, D.L., Prince, J.L., Landman, B.A., Combining multi-atlas segmentation with brain surface estimation. SPIE Medical Imaging, 2016, International Society for Optics and Photonics, 97840E–997848.
46. Iglesias, J.E., Sabuncu, M.R., Multi-atlas segmentation of biomedical images: a survey. Med. Image Anal. 24 (2015), 205–219.
47. Isgum, I., Staring, M., Rutten, A., Prokop, M., Viergever, M.A., van Ginneken, B., Multi-atlas-based segmentation with local decision fusion–application to cardiac and aortic segmentation in CT scans. IEEE Trans. Med. Imaging 28 (2009), 1000–1010.
48. Jog, A., Carass, A., Roy, S., Pham, D.L., Prince, J.L., MR image synthesis by contrast learning on neighborhood ensembles. Med. Image Anal. 24 (2015), 63–76.
49. Keshavan, M.S., Anderson, S., Beckwith, C., Nash, K., Pettegrew, J.W., Krishnan, K.R., A comparison of stereology and segmentation techniques for volumetric measurements of lateral ventricles in magnetic resonance imaging. Psychiatry Res. 61 (1995), 53–60.
50. Kim, J.S., Singh, V., Lee, J.K., Lerch, J., Ad-Dab'bagh, Y., MacDonald, D., Lee, J.M., Kim, S.I., Evans, A.C., Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification. NeuroImage 27 (2005), 210–221.
51. Klein, A., Dal Canton, T., Ghosh, S.S., Landman, B., Lee, J., Worth, A., Open labels: online feedback for a public resource of manually labeled brain images. 16th Annual Meeting for the Organization of Human Brain Mapping, 2010.
52. Landman, B., Warfield, S., MICCAI 2012 workshop on multi-atlas labeling. Medical Image Computing and Computer Assisted Intervention Conference 2012: MICCAI 2012 Grand Challenge and Workshop on Multi-Atlas Labeling Challenge Results, 2012.
53. Landman, B.A., Bogovic, J.A., Carass, A., Chen, M., Roy, S., Shiee, N., Yang, Z., Kishore, B., Pham, D., Bazin, P.L., Resnick, S.M., Prince, J.L., System for integrated neuroimaging analysis and processing of structure. Neuroinformatics 11 (2013), 91–103.
54. Landman, B.A., Huang, A.J., Gifford, A., Vikram, D.S., Lim, I.A., Farrell, J.A., Bogovic, J.A., Hua, J., Chen, M., Jarso, S., Smith, S.A., Joel, S., Mori, S., Pekar, J.J., Barker, P.B., Prince, J.L., van Zijl, P.C., Multi-parametric neuroimaging reproducibility: a 3-T resource study. NeuroImage 54 (2011), 2854–2866.
55. Lehmann, M., Douiri, A., Kim, L.G., Modat, M., Chan, D., Ourselin, S., Barnes, J., Fox, N.C., Atrophy patterns in Alzheimer's disease and semantic dementia: a comparison of FreeSurfer and manual volumetric measurements. NeuroImage 49 (2010), 2264–2274.
56. Li, B., Bryan, F., Landman, B.A., Next generation of the java image science toolkit (JIST): visualization and validation. Insight J 2012 (2012), 1–16.
57. Lim, S.P., Haron, H., Surface reconstruction techniques: a review. Artif. Intell. Rev. 42 (2014), 59–78.
58. Liu, T., Nie, J., Tarokh, A., Guo, L., Wong, S.T., Reconstruction of central cortical surface from brain MRI images: method and application. NeuroImage 40 (2008), 991–1002.
59. Lucas, B.C., Bogovic, J.A., Carass, A., Bazin, P.L., Prince, J.L., Pham, D.L., Landman, B.A., The Java Image Science Toolkit (JIST) for rapid prototyping and publishing of neuroimaging software. Neuroinformatics 8 (2010), 5–17.
60. Lyttelton, O., Boucher, M., Robbins, S., Evans, A., An unbiased iterative group registration template for cortical surface analysis. NeuroImage 34 (2007), 1535–1544.
61. MacDonald, D., Kabani, N., Avis, D., Evans, A.C., Automated 3-D extraction of inner and outer surfaces of cerebral cortex from MRI. NeuroImage 12 (2000), 340–356.
62. Marcus, D.S., Wang, T.H., Parker, J., Csernansky, J.G., Morris, J.C., Buckner, R.L., Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J. Cogn. Neurosci. 19 (2007), 1498–1507.
63. Osher, S., Fedkiw, R., Level Set Methods and Dynamic Implicit Surfaces. 2006, Springer Science & Business Media.
64. Osher, S., Sethian, J.A., Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J. Comput. Phys. 79 (1988), 12–49.
65. Ourselin, S., Roche, A., Subsol, G., Pennec, X., Ayache, N., Reconstructing a 3D structure from serial histological sections. Image Vis. Comput. 19 (2001), 25–31.
66. Pham, D.L., Prince, J.L., Adaptive fuzzy segmentation of magnetic resonance images. IEEE Trans. Med. Imaging 18 (1999), 737–752.
67. Plassard, A.J., Harrigan, R.L., Newton, A.T., Rane, S., Pallavaram, S., D'Haese, P.F., Dawant, B.M., Claassen, D.O., Landman, B.A., On the fallacy of quantitative segmentation for T1-weighted MRI. SPIE Medical Imaging, 2016, International Society for Optics and Photonics, 978416–978417.
68. Plassard, A.J., Hinton, K.E., Venkatraman, V., Gonzalez, C., Resnick, S.M., Landman, B.A., Evaluation of atlas-based white matter segmentation with Eve. Proc. SPIE Int. Soc. Opt. Eng., 2015, 9413.
69. Resnick, S.M., Pham, D.L., Kraut, M.A., Zonderman, A.B., Davatzikos, C., Longitudinal magnetic resonance imaging studies of older adults: a shrinking brain. J. Neurosci. 23 (2003), 3295–3301.
70. Rohlfing, T., Russakoff, D.B., Maurer, C.R. Jr., Performance-based classifier combination in atlas-based image segmentation using expectation–maximization parameter estimation. IEEE Trans. Med. Imaging 23 (2004), 983–994.
71. Sabuncu, M.R., Yeo, B.T., Van Leemput, K., Fischl, B., Golland, P., A generative model for image segmentation based on label fusion. IEEE Trans. Med. Imaging 29 (2010), 1714–1729.
72. Sethian, J.A., Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science. 1999, Cambridge university press.
73. Shattuck, D.W., Leahy, R.M., BrainSuite: an automated cortical surface identification tool. Med. Image Anal. 6 (2002), 129–142.
74. Shiee, N., Bazin, P.L., Cuzzocreo, J.L., Ye, C., Kishore, B., Carass, A., Calabresi, P.A., Reich, D.S., Prince, J.L., Pham, D.L., Reconstruction of the human cerebral cortex robust to white matter lesions: method and validation. Hum. Brain Mapp. 35 (2014), 3385–3401.
75. Shiee, N., Bazin, P.L., Ozturk, A., Reich, D.S., Calabresi, P.A., Pham, D.L., A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions. NeuroImage 49 (2010), 1524–1535.
76. Shock, N.W., Greulich, R.C., Andres, R., Arenberg, D., Costa, P.T., Lakatta, E.G., Tobin, J.D., Normal human aging: the Baltimore longitudinal study of aging. 84, 1984, NIH Publication, 2450.
77. Stewart, J.C., Zielke, D.J., Hawkins, M.A., Williams, D.R., Carnethon, M.R., Knox, S.S., Matthews, K.A., Depressive symptom clusters and 5-year incidence of coronary artery calcification: the coronary artery risk development in young adults study. Circulation 126 (2012), 410–417.
78. Symms, M., Jager, H.R., Schmierer, K., Yousry, T.A., A review of structural magnetic resonance neuroimaging. J. Neurol. Neurosurg. Psychiatry 75 (2004), 1235–1244.
79. Thompson, P.M., Mega, M.S., Woods, R.P., Zoumalan, C.I., Lindshield, C.J., Blanton, R.E., Moussai, J., Holmes, C.J., Cummings, J.L., Toga, A.W., Cortical change in Alzheimer's disease detected with a disease-specific population-based brain atlas. Cereb. Cortex 11 (2001), 1–16.
80. Tosun, D., Prince, J.L., A geometry-driven optical flow warping for spatial normalization of cortical surfaces. IEEE Trans. Med. Imaging 27 (2008), 1739–1753.
81. Tosun, D., Rettmann, M.E., Prince, J.L., Mapping techniques for aligning sulci across multiple brains. Med. Image Anal. 8 (2004), 295–309.
82. Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y., Egan, A., Yushkevich, P.A., Gee, J.C., N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29 (2010), 1310–1320.
83. Tustison, N.J., Cook, P.A., Klein, A., Song, G., Das, S.R., Duda, J.T., Kandel, B.M., van Strien, N., Stone, J.R., Gee, J.C., Avants, B.B., Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements. NeuroImage 99 (2014), 166–179.
84. Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P., Automated model-based tissue classification of MR images of the brain. IEEE Trans. Med. Imaging 18 (1999), 897–908.
85. Wang, H.Z., Suh, J.W., Das, S.R., Pluta, J.B., Craige, C., Yushkevich, P.A., Multi-atlas segmentation with joint label fusion. IEEE Trans. Pattern Anal. Mach. Intell. 35 (2013), 611–623.
86. Warfield, S.K., Zou, K.H., Wells, W.M., Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans. Med. Imaging 23 (2004), 903–921.
87. Wells, W.M., Grimson, W.L., Kikinis, R., Jolesz, F.A., Adaptive segmentation of MRI data. IEEE Trans. Med. Imaging 15 (1996), 429–442.
88. Wilcoxon, F., Individual comparisons by ranking methods. Biom. Bull., 1945, 80–83.
89. Winkler, A.M., Sabuncu, M.R., Yeo, B.T., Fischl, B., Greve, D.N., Kochunov, P., Nichols, T.E., Blangero, J., Glahn, D.C., Measuring and comparing brain cortical surface area and other areal quantities. NeuroImage 61 (2012), 1428–1443.
90. Wu, M., Rosano, C., Lopez-Garcia, P., Carter, C.S., Aizenstein, H.J., Optimum template selection for atlas-based segmentation. NeuroImage 34 (2007), 1612–1618.
91. Xu, C., Prince, J.L., Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 7 (1998), 359–369.
92. 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 (1999), 467–480.
93. Yeo, B.T.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 (2010), 650–668.
94. Zeng, X., 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 (1999), 927–937.