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Automatic corpus callosum segmentation using a deformable active Fourier contour model.

Clement Vachet1, Benjamin Yvernault1, Kshamta Bhatt1

  • 1Department of Psychiatry, University of North Carolina at Chapel Hill, NC, USA.

Proceedings of Spie--The International Society for Optical Engineering
|December 20, 2013
PubMed
Summary
This summary is machine-generated.

We developed CCSeg, an automated framework for segmenting the corpus callosum (CC) and its subdivisions in MRI scans. This tool demonstrates high reliability and is crucial for studying neurodevelopmental conditions like autism.

Keywords:
Fourier coefficientcorpus callosumsegmentationshape model

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Developmental Neuroscience

Background:

  • The corpus callosum (CC) is vital for interhemispheric communication and is frequently studied in neurodevelopmental disorders, including autism.
  • Accurate segmentation of the CC and its subdivisions is essential for quantitative neuroimaging analysis.

Purpose of the Study:

  • To present a novel, automated framework for the segmentation of the corpus callosum and its lobar subdivisions.
  • To validate the reliability and utility of this framework in neuroimaging research.

Main Methods:

  • Developed an automated segmentation framework (CCSeg) using constrained elastic deformation of a 2D Fourier descriptor-based Active Shape Model.
  • Utilized a principal component shape space derived from 150+ subjects and MNI-aligned T1w MRI data.
  • Implemented a multi-step optimization strategy and a probabilistic model for lobar parcellation, integrated into an open-source application.

Main Results:

  • Achieved automatic segmentation of the corpus callosum and its lobar subdivisions.
  • Demonstrated superb intra-class correlation coefficient (0.99) for reliability in a pediatric dataset.
  • Integrated the framework into the open-source CCSeg application with both command-line and GUI interfaces.

Conclusions:

  • The CCSeg framework provides a reliable and automated method for corpus callosum segmentation and parcellation.
  • This tool facilitates advanced analysis in large-scale neuroimaging studies, particularly for pediatric brain development and autism research.
  • CCSeg is currently employed in a major longitudinal study on autism and brain development.