Automated 3-D extraction of inner and outer surfaces of cerebral cortex from MRI. Export

@article{citeulike:563642, abstract = {Automatic computer processing of large multidimensional images such as those produced by magnetic resonance imaging (MRI) is greatly aided by deformable models, which are used to extract, identify, and quantify specific neuroanatomic structures. A general method of deforming polyhedra is presented here, with two novel features. First, explicit prevention of self-intersecting surface geometries is provided, unlike conventional deformable models, which use regularization constraints to discourage but not necessarily prevent such behavior. Second, deformation of multiple surfaces with intersurface proximity constraints allows each surface to help guide other surfaces into place using model-based constraints such as expected thickness of an anatomic surface. These two features are used advantageously to identify automatically the total surface of the outer and inner boundaries of cerebral cortical gray matter from normal human MR images, accurately locating the depths of the sulci, even where noise and partial volume artifacts in the image obscure the visibility of sulci. The extracted surfaces are enforced to be simple two-dimensional manifolds (having the topology of a sphere), even though the data may have topological holes. This automatic 3-D cortex segmentation technique has been applied to 150 normal subjects, simultaneously extracting both the gray/white and gray/cerebrospinal fluid interface from each individual. The collection of surfaces has been used to create a spatial map of the mean and standard deviation for the location and the thickness of cortical gray matter. Three alternative criteria for defining cortical thickness at each cortical location were developed and compared. These results are shown to corroborate published postmortem and in vivo measurements of cortical thickness.}, address = {McConnell Brain Imaging Centre, Webster 2B, Montr\'{e}al Neurological Institute, Qu\'{e}bec, Montr\'{e}al, 3801 University, H3A 2B4, Canada.}, author = {MacDonald, D. and Kabani, N. and Avis, D. and Evans, A. C.}, citeulike-article-id = {563642}, citeulike-linkout-0 = {http://dx.doi.org/10.1006/nimg.1999.0534}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S1053-8119(99)90534-7}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/10944416}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=10944416}, doi = {10.1006/nimg.1999.0534}, issn = {1053-8119}, journal = {Neuroimage}, keywords = {2002, brain, doctorate2, mri, surface}, month = {September}, number = {3}, pages = {340--356}, posted-at = {2009-03-15 13:06:15}, priority = {2}, title = {Automated 3-D extraction of inner and outer surfaces of cerebral cortex from MRI.}, url = {http://dx.doi.org/10.1006/nimg.1999.0534}, volume = {12}, year = {2000} }

TY - JOUR ID - citeulike:563642 L3 - citeulike-article-id:563642 N2 - Automatic computer processing of large multidimensional images such as those produced by magnetic resonance imaging (MRI) is greatly aided by deformable models, which are used to extract, identify, and quantify specific neuroanatomic structures. A general method of deforming polyhedra is presented here, with two novel features. First, explicit prevention of self-intersecting surface geometries is provided, unlike conventional deformable models, which use regularization constraints to discourage but not necessarily prevent such behavior. Second, deformation of multiple surfaces with intersurface proximity constraints allows each surface to help guide other surfaces into place using model-based constraints such as expected thickness of an anatomic surface. These two features are used advantageously to identify automatically the total surface of the outer and inner boundaries of cerebral cortical gray matter from normal human MR images, accurately locating the depths of the sulci, even where noise and partial volume artifacts in the image obscure the visibility of sulci. The extracted surfaces are enforced to be simple two-dimensional manifolds (having the topology of a sphere), even though the data may have topological holes. This automatic 3-D cortex segmentation technique has been applied to 150 normal subjects, simultaneously extracting both the gray/white and gray/cerebrospinal fluid interface from each individual. The collection of surfaces has been used to create a spatial map of the mean and standard deviation for the location and the thickness of cortical gray matter. Three alternative criteria for defining cortical thickness at each cortical location were developed and compared. These results are shown to corroborate published postmortem and in vivo measurements of cortical thickness. IS - 3 JF - Neuroimage SN - 1053-8119 AD - McConnell Brain Imaging Centre, Webster 2B, Montréal Neurological Institute, Québec, Montréal, 3801 University, H3A 2B4, Canada. EP - 356 TI - Automated 3-D extraction of inner and outer surfaces of cerebral cortex from MRI. VL - 12 SP - 340 KW - 2002 KW - brain KW - doctorate2 KW - mri KW - surface AU - MacDonald, D AU - Kabani, N AU - Avis, D AU - Evans, AC PY - 2000/09// UR - http://dx.doi.org/10.1006/nimg.1999.0534 ER -