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. 2012 Mar 23:8317.
doi: 10.1117/12.911504.

Automatic corpus callosum segmentation using a deformable active Fourier contour model

Clement Vachet  1 , Benjamin Yvernault  1 , Kshamta Bhatt  1 , Rachel G Smith  2 , Guido Gerig  3 , Heather Cody Hazlett  2 , Martin Styner  4
Affiliations

Automatic corpus callosum segmentation using a deformable active Fourier contour model

Clement Vachet et al. Proc SPIE Int Soc Opt Eng. .

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.

Keywords: Fourier coefficient; corpus callosum; segmentation; shape model.

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Figures

Figure 1
Figure 1
Corpus callosum segmentation framework
Figure 2
Figure 2
Shape model: major modes of variations (left-right: 1-3 mode)
Figure 3
Figure 3
Mean shape model (bold) and its 150+ training set (fine)
Figure 4
Figure 4
Image gray-value information is extracted along the contour
Figure 5
Figure 5
Semi-interactive corpus callosum segmentation using a repulsion point
Figure 6
Figure 6
Corpus callosum in an MR image (left) with Witelson subdivision and its neuro-histological motivation (right)
Figure 7
Figure 7
Subdivision model computation. Left: DTI fiber bundles associated by lobes. Middle: Schematic visualization of the probability computation. Right: Sample CC contour probability map plotting disks of radii relative to the corresponding probability at each contour point
Figure 8
Figure 8
Probabilistic area maps for a sample case. Each region is annotated with the respective probabilistic area percentage relative to the overall area
Figure 9
Figure 9
Automatic corpus callosum segmentation on 1-year-old subjects
See this image and copyright information in PMC

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