Automatic corpus callosum segmentation using a deformable active Fourier contour model

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Publication Type pre-print
School or College <blank>
Department <blank>
Creator Gerig, Guido
Other Author Vachet, Clement; Yvernault, Benjamin; Bhatt, Kshamta; Smith, Rachel G.; Hazlett, Heather C.; Styner, Martin
Title Automatic corpus callosum segmentation using a deformable active Fourier contour model
Date 2012-01-01
Description The corpus callosum (CC) is a structure of interest in many neuroimaging studies of neuro-developmental pathology such as autism. It plays an integral role in relaying sensory, motor and cognitive information from homologous regions in both hemispheres. We have developed a framework that allows automatic segmentation of the corpus callosum and its lobar subdivisions. Our approach employs constrained elastic deformation of flexible Fourier contour model, and is an extension of Szekely's 2D Fourier descriptor based Active Shape Model. The shape and appearance model, derived from a large mixed population of 150+ subjects, is described with complex Fourier descriptors in a principal component shape space. Using MNI space aligned T1w MRI data, the CC segmentation is initialized on the mid-sagittal plane using the tissue segmentation. A multi-step optimization strategy, with two constrained steps and a final unconstrained step, is then applied. If needed, interactive segmentation can be performed via contour repulsion points. Lobar connectivity based parcellation of the corpus callosum can finally be computed via the use of a probabilistic CC subdivision model. Our analysis framework has been integrated in an open-source, end-to-end application called CCSeg both with a command line and Qt-based graphical user interface (available on NITRC). A study has been performed to quantify the reliability of the semi-automatic segmentation on a small pediatric dataset. Using 5 subjects randomly segmented 3 times by two experts, the intra-class correlation coefficient showed a superb reliability (0.99). CCSeg is currently applied to a large longitudinal pediatric study of brain development in autism.
Type Text
Publisher International Society for Optical Engineering (SPIE)
Volume 8317
First Page 831707-1
Last Page 831707-7
Language eng
Bibliographic Citation Vachet, C., Yvernault, B., Bhatt, K., Smith, R. G., Gerig, G., Hazlett, H. C., & Styner, M. (2012). Automatic corpus callosum segmentation using a deformable active Fourier contour model. Structural, and Functional Imaging, SPIE, 8317, 831707-1-831707-7.
Rights Management (c)Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. http://dx.doi.org/[INSERT DOI]
Format Medium application/pdf
Format Extent 1,221,985 bytes
Identifier uspace,19178
ARK ark:/87278/s6qn9gwf
Setname ir_uspace
Date Created 2015-01-24
Date Modified 2015-01-27
ID 712783
Reference URL https://collections.lib.utah.edu/ark:/87278/s6qn9gwf
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