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Cortical Correspondence via Sulcal Curve-constrained Spherical Registration with Application to Macaque Studies

Institution:
1Department of Computer Science, University of North Carolina at Chapel Hill, NC, USA.
2Department of Psychiatry, University of North Carolina at Chapel Hill, NC, USA.
3Computer Science and Engineering, Soongsil University, Seoul, South Korea.
4Computer Science, KAIST, Daejeon, South Korea.
5Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
6Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA.
Publication Date:
Mar-2013
Journal:
Proc Soc Photo Opt Instrum Eng
Volume Number:
8669
Citation:
Proc Soc Photo Opt Instrum Eng. 2013 Mar 13;8669.
PubMed ID:
24357916
PMCID:
PMC3865241
Keywords:
Cortical Correspondence, spherical harmonics, sulcal curve, surface registration
Appears in Collections:
NA-MIC
Sponsors:
P30 HD003110/HD/NICHD NIH HHS/United States
P50 MH078105/MH/NIMH NIH HHS/United States
R01 MH091645/MH/NIMH NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Lyu I., Kim S.H., Seong J-K., Yoo S.W., Evans A.C., Shi Y., Sanchez M., Niethammer M., Styner M. Cortical Correspondence via Sulcal Curve-constrained Spherical Registration with Application to Macaque Studies. Proc Soc Photo Opt Instrum Eng. 2013 Mar 13;8669. PMID: 24357916. PMCID: PMC3865241.
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In this work, we present a novel cortical correspondence method with application to the macaque brain. The correspondence method is based on sulcal curve constraints on a spherical deformable registration using spherical harmonics to parameterize the spherical deformation. Starting from structural MR images, we first apply existing preprocessing steps: brain tissue segmentation using the Automatic Brain Classification tool (ABC), as well as cortical surface reconstruction and spherical parametrization of the cortical surface via Constrained Laplacian-based Automated Segmentation with Proximities (CLASP). Then, initial correspondence between two cortical surfaces is automatically determined by a curve labeling method using sulcal landmarks extracted along sulcal fundic regions. Since the initial correspondence is limited to sulcal regions, we use spherical harmonics to extrapolate and regularize this correspondence to the entire cortical surface. To further improve the correspondence, we compute a spherical registration that optimizes the spherical harmonic parameterized deformation using a metric that incorporates the error over the sulcal landmarks as well as the normalized cross correlation of sulcal depth maps over the whole cortical surface. For evaluation, a normal 18-months-old macaque brain (for both left and right hemispheres) was matched to a prior macaque brain template with 9 manually labeled, major sulcal curves. The results show successful registration using the proposed registration approach. Evaluation results for optimal parameter settings are presented as well.

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