
Proceedings Paper
An automated pipeline for cortical surface generation and registration of the cerebral cortexFormat | Member Price | Non-Member Price |
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Paper Abstract
The human cerebral cortex is one of the most complicated structures in the body. It has a highly convoluted
structure with much of the cortical sheet buried in sulci. Based on cytoarchitectural and functional imaging
studies, it is possible to segment the cerebral cortex into several subregions. While it is only possible to differentiate
the true anatomical subregions based on cytoarchitecture, the surface morphometry aligns closely with the
underlying cytoarchitecture and provides features that allow the surface of the cortex to be parcellated based on
the sulcal and gyral patterns that are readily visible on the MR images.
We have developed a fully automated pipeline for the generation and registration of cortical surfaces in
the spherical domain. The pipeline initiates with the BRAINS AutoWorkup pipeline. Subsequently, topology
correction and surface generation is performed to generate a genus zero surface and mapped to a sphere. Several
surface features are then calculated to drive the registration between the atlas surface and other datasets. A
spherical diffeomorphic demons algorithm is used to co-register an atlas surface onto a subject surface.
A lobar based atlas of the cerebral cortex was created from a manual parcellation of the cortex. The atlas
surface was then co-registered to five additional subjects using a spherical diffeomorphic demons algorithm. The
labels from the atlas surface were warped on the subject surface and compared to the manual raters. The average
Dice overlap index was 0.89 across all regions.
Paper Details
Date Published: 11 March 2011
PDF: 7 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 796229 (11 March 2011); doi: 10.1117/12.876509
Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)
PDF: 7 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 796229 (11 March 2011); doi: 10.1117/12.876509
Show Author Affiliations
Wen Li, Univ. of Iowa (United States)
Luis Ibanez, Kitware, Inc. (United States)
Arnaud Gelas, Harvard Medical School (United States)
B. T. Thomas Yeo, Harvard Univ. (United States)
Luis Ibanez, Kitware, Inc. (United States)
Arnaud Gelas, Harvard Medical School (United States)
B. T. Thomas Yeo, Harvard Univ. (United States)
Marc Niethammer, Univ. of North Carolina at Chapel Hill (United States)
Nancy C. Andreasen, The Univ. of Iowa (United States)
Vincent A. Magnotta, The Univ. of Iowa (United States)
Nancy C. Andreasen, The Univ. of Iowa (United States)
Vincent A. Magnotta, The Univ. of Iowa (United States)
Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)
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