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Author (up) Vachet, C.; Yvernault, B.; Bhatt, K.; Smith, R.G.; Gerig, G.; Hazlett, H.C.; Styner, M. url  doi
  Title Automatic corpus callosum segmentation using a deformable active Fourier contour model Type Journal Article
  Year 2012 Publication Proceedings of SPIE--the International Society for Optical Engineering Abbreviated Journal Proc SPIE Int Soc Opt Eng  
  Volume 8317 Issue Pages  
  Keywords Fourier coefficient; corpus callosum; segmentation; shape model  
  Abstract 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.  
  Address Department of Psychiatry, University of North Carolina at Chapel Hill, NC, USA ; Department of Computer Sciences, University of North Carolina at Chapel Hill, NC, USA  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1018-4732 ISBN Medium  
  Area Expedition Conference  
  Notes PMID:24353382 Approved no  
  Call Number ref @ user @ Serial 90673  
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