TY - JOUR
T1 - Using a statistical shape model to extract sulcal curves on the outer cortex of the human brain
AU - Tao, Xiaodong
AU - Prince, Jerry L.
AU - Davatzikos, Christos
N1 - Funding Information:
Manuscript received October 30, 2001; revised February 27, 2002. This work was supported in part by the National Institutes of Health (NIH) under Grant R01AG14971, Contract N01AG32129, and Grant R01NS37747 and in part by the National Science Foundation (NSF) under ERC Grant CISST#9731748. Asterisk indicates corresponding author.
PY - 2002/5
Y1 - 2002/5
N2 - A method for automated segmentation of major cortical sulci on the outer brain boundary is presented, with emphasis on automatically determining point correspondence and on labeling cortical regions. The method is formulated in a general optimization framework defined on the unit sphere, which serves as parametric domain for convoluted surfaces of spherical topology. A statistical shape model, which includes a network of deformable curves on the unit sphere, seeks geometric features such as high curvature regions and labels such features via a deformation process that is confined within a spherical map of the outer brain boundary. The limitations of the customary spherical coordinate system, which include discontinuities at the poles and nonuniform sampling, are overcome by defining the statistical prior of shape variation in terms of projections of landmark points onto corresponding tangent planes of the sphere. The method is tested against and shown to be as accurate as manually defined segmentations.
AB - A method for automated segmentation of major cortical sulci on the outer brain boundary is presented, with emphasis on automatically determining point correspondence and on labeling cortical regions. The method is formulated in a general optimization framework defined on the unit sphere, which serves as parametric domain for convoluted surfaces of spherical topology. A statistical shape model, which includes a network of deformable curves on the unit sphere, seeks geometric features such as high curvature regions and labels such features via a deformation process that is confined within a spherical map of the outer brain boundary. The limitations of the customary spherical coordinate system, which include discontinuities at the poles and nonuniform sampling, are overcome by defining the statistical prior of shape variation in terms of projections of landmark points onto corresponding tangent planes of the sphere. The method is tested against and shown to be as accurate as manually defined segmentations.
KW - Automatic sulcal extraction
KW - Outer cortex
KW - Statistical shape model
KW - Unit sphere
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U2 - 10.1109/TMI.2002.1009387
DO - 10.1109/TMI.2002.1009387
M3 - Conference article
C2 - 12071622
AN - SCOPUS:0036557910
SN - 0278-0062
VL - 21
SP - 513
EP - 524
JO - IEEE transactions on medical imaging
JF - IEEE transactions on medical imaging
IS - 5
T2 - 17th International Conference on Information Processing in Medical Imaging (IPMI'01)
Y2 - 1 June 2001 through 1 June 2001
ER -