Automated graph-based analysis and correction of cortical volume topology

The human cerebral cortex is topologically equivalent to a sheet and can be considered topologically spherical if it is closed at the brainstem. Low-level segmentation of magnetic resonance (MR) imagery typically produces cerebral volumes whose tessellations are not topologically spherical. The authors present a novel algorithm that analyzes and constrains the topology of a volumetric object. Graphs are formed that represent the connectivity of voxel segments in the foreground and background of the image. These graphs are analyzed and minimal corrections to the volume are made prior to tessellation. The authors apply the algorithm to a simple test object and to cerebral white matter masks generated by a low-level tissue identification sequence. The authors tessellate the resulting objects using the marching cubes algorithm and verify their topology by computing their Euler characteristics. A key benefit of the algorithm is that it localizes the change to a volume to the specific areas of its topological defects.

[1]  R. Woods,et al.  Mathematical/computational challenges in creating deformable and probabilistic atlases of the human brain , 2000, Human brain mapping.

[2]  M I Miller,et al.  Mathematical textbook of deformable neuroanatomies. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Paul M. Thompson,et al.  A surface-based technique for warping three-dimensional images of the brain , 1996, IEEE Trans. Medical Imaging.

[4]  Abraham Z. Snyder,et al.  Surface-Based Analyses of the Human Cerebral Cortex , 1999 .

[5]  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.

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

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

[8]  Richard M. Leahy,et al.  BrainSuite: An Automated Cortical Surface Identification Tool , 2000, MICCAI.

[9]  Jerry L. Prince,et al.  Adaptive fuzzy segmentation of magnetic resonance images , 1999, IEEE Transactions on Medical Imaging.

[10]  D. V. van Essen,et al.  Computerized Mappings of the Cerebral Cortex: A Multiresolution Flattening Method and a Surface-Based Coordinate System , 1996, Journal of Cognitive Neuroscience.

[11]  Richard M. Leahy,et al.  BrainSuite: An Automated Cortical Surface Identification Tool , 2000, MICCAI.

[12]  Junaed Sattar Snakes , Shapes and Gradient Vector Flow , 2022 .

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

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

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

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

[17]  D. Louis Collins,et al.  ANIMAL+INSECT: Improved Cortical Structure Segmentation , 1999, IPMI.

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

[19]  A. Toga,et al.  A SURFACE-BASED TECHNIQUE FOR WARPING 3-DIMENSIONAL IMAGES OF THE BRAIN , 2000 .

[20]  Gary E. Christensen,et al.  Consistent Linear-Elastic Transformations for Image Matching , 1999, IPMI.

[21]  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.

[22]  R. Leahy,et al.  Magnetic Resonance Image Tissue Classification Using a Partial Volume Model , 2001, NeuroImage.

[23]  Jerry L. Prince,et al.  Reconstruction of the Central Layer of the Human Cerebral Cortex from MR Images , 1998, MICCAI.

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

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

[26]  R M Leahy,et al.  Imaging neural activity using MEG and EEG. , 1997, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

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

[28]  A. Toga,et al.  Detection and mapping of abnormal brain structure with a probabilistic atlas of cortical surfaces. , 1997, Journal of computer assisted tomography.

[29]  Eric W. Weisstein,et al.  The CRC concise encyclopedia of mathematics , 1999 .

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

[31]  Guido Gerig,et al.  Elastic model-based segmentation of 3-D neuroradiological data sets , 1999, IEEE Transactions on Medical Imaging.