Topology correction in brain cortex segmentation using a multiscale, graph-based algorithm

Reconstructing an accurate and topologically correct representation of the cortical surface of the brain is an important objective in various neuroscience applications. Most cortical surface reconstruction methods either ignore topology or correct it using manual editing or methods that lead to inaccurate reconstructions. Shattuck and Leahy (1999, 2000) recently reported a fully automatic method that yields a topologically correct representation with little distortion of the underlying segmentation. We provide an alternate approach that has several advantages over their approach, including the use of arbitrary digital connectivities, a flexible morphology-based multiscale approach, and the option of foreground-only or background-only correction. A detailed analysis of the method's performance on 15 magnetic resonance brain images is provided.

[1]  Jane Wilhelms,et al.  Topological considerations in isosurface generation , 1994, TOGS.

[2]  I. Rossman,et al.  Normal Human Aging: The Baltimore Longitudinal Study of Aging , 1986 .

[3]  Gilles Bertrand,et al.  A 3D-hole closing algorithm , 1996 .

[4]  J. Maunsell,et al.  Two‐dimensional maps of the cerebral cortex , 1980, The Journal of comparative neurology.

[5]  S. Resnick,et al.  One-year age changes in MRI brain volumes in older adults. , 2000, Cerebral cortex.

[6]  Guillermo Sapiro,et al.  Creating connected representations of cortical gray matter for functional MRI visualization , 1997, IEEE Transactions on Medical Imaging.

[7]  Robert Sedgewick,et al.  Algorithms in C , 1990 .

[8]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[9]  Gilles Bertrand,et al.  A new characterization of three-dimensional simple points , 1994, Pattern Recognition Letters.

[10]  C. Davatzikos,et al.  Using a deformable surface model to obtain a shape representation of the cortex , 1995, Proceedings of International Symposium on Computer Vision - ISCV.

[11]  Richard M. Leahy,et al.  Topologically constrained cortical surfaces from MRI , 2000, Medical Imaging: Image Processing.

[12]  A. Toga,et al.  Cortical variability and asymmetry in normal aging and Alzheimer's disease. , 1998, Cerebral cortex.

[13]  Richard M. Leahy,et al.  Topological refinement of volumetric data , 1999, Medical Imaging.

[14]  A. Dale,et al.  Improved Localizadon of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: A Linear Approach , 1993, Journal of Cognitive Neuroscience.

[15]  Bidyut Baran Chaudhuri,et al.  Detection of 3-D Simple Points for Topology Preserving Transformations with Application to Thinning , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Nicholas Ayache,et al.  Topological segmentation of discrete surfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  Azriel Rosenfeld,et al.  Digital topology: Introduction and survey , 1989, Comput. Vis. Graph. Image Process..

[18]  David Arenberg,et al.  Normal Human Aging: The Baltimore Longitudinal Study on Aging , 1984 .

[19]  Stacia R. Engel,et al.  Creating images of the flattened cortical sheet , 1996 .

[20]  D. V. van Essen,et al.  Structural and Functional Analyses of Human Cerebral Cortex Using a Surface-Based Atlas , 1997, The Journal of Neuroscience.

[21]  Robert T. Schultz,et al.  Segmentation and Measurement of the Cortex from 3D MR Images , 1998, MICCAI.

[22]  Anders M. Dale,et al.  Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex , 2001, IEEE Transactions on Medical Imaging.

[23]  Xiao Han,et al.  Cortical surface reconstruction using a topology preserving geometric deformable model , 2001, Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2001).

[24]  Demetri Terzopoulos,et al.  Deformable models in medical image analysis: a survey , 1996, Medical Image Anal..

[25]  Alan C. Evans,et al.  Automated 3-D Extraction of Inner and Outer Surfaces of Cerebral Cortex from MRI , 2000, NeuroImage.

[26]  Jerry L. Prince,et al.  Reconstruction of the human cerebral cortex from magnetic resonance images , 1999, IEEE Transactions on Medical Imaging.

[27]  Jerry L. Prince,et al.  Graph-Based Topology Correction for Brain Cortex Segmentation , 2001, IPMI.

[28]  Max K. Agoston.  Algebraic topology: A first course , 1976 .

[29]  Paul Ning,et al.  An evaluation of implicit surface tilers , 1993, IEEE Computer Graphics and Applications.

[30]  H A Drury,et al.  Computational methods for reconstructing and unfolding the cerebral cortex. , 1995, Cerebral cortex.

[31]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[32]  Anders M. Dale,et al.  Improved Localization of Cortical Activity By Combining EEG and MEG with MRI Cortical Surface Reconstruction , 2002 .

[33]  Christos Davatzikos,et al.  Finding parametric representations of the cortical sulci using an active contour model , 1997, Medical Image Anal..

[34]  A. Dale,et al.  Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System , 1999, NeuroImage.

[35]  Gilles Bertrand,et al.  Simple points, topological numbers and geodesic neighborhoods in cubic grids , 1994, Pattern Recognit. Lett..

[36]  R. Kikinis,et al.  Temporal lobe sulco-gyral pattern anomalies in schizophrenia: an in vivo MR three-dimensional surface rendering study , 1994, Neuroscience Letters.

[37]  Xiao Han,et al.  A topology preserving deformable model using level sets , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[38]  Gilles Bertrand,et al.  A Boolean characterization of three-dimensional simple points , 1996, Pattern Recognition Letters.

[39]  Xiao Han,et al.  Statistical Study on Cortical Sulci of Human Brains , 2001, IPMI.

[40]  Christos Davatzikos,et al.  Hierarchical Matching of Cortical Features for Deformable Brain Image Registration , 1999, IPMI.

[41]  Zoë J. Wood,et al.  Topological Noise Removal , 2001, Graphics Interface.

[42]  Bernd Hamann,et al.  The asymptotic decider: resolving the ambiguity in marching cubes , 1991, Proceeding Visualization '91.