Automatic corpus callosum segmentation using a deformable active Fourier contour model

Clement Vachet, Benjamin Yvernault, Kshamta Bhatt, Rachel G. Smith, Guido Gerig, Heather Cody Hazlett, Martin Styner

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    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.

    Original languageEnglish (US)
    Title of host publicationMedical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging
    Volume8317
    DOIs
    StatePublished - 2012
    EventMedical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging - San Diego, CA, United States
    Duration: Feb 5 2012Feb 7 2012

    Other

    OtherMedical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging
    CountryUnited States
    CitySan Diego, CA
    Period2/5/122/7/12

    Fingerprint

    Corpus Callosum
    Pediatrics
    subdivisions
    Autistic Disorder
    Neuroimaging
    graphical user interface
    elastic deformation
    pathology
    Elastic deformation
    commands
    Pathology
    Graphical user interfaces
    hemispheres
    correlation coefficients
    Magnetic resonance imaging
    brain
    Brain
    Longitudinal Studies
    Tissue
    optimization

    Keywords

    • Corpus callosum
    • Fourier coefficient
    • Segmentation
    • Shape model

    ASJC Scopus subject areas

    • Atomic and Molecular Physics, and Optics
    • Electronic, Optical and Magnetic Materials
    • Biomaterials
    • Radiology Nuclear Medicine and imaging

    Cite this

    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. In Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging (Vol. 8317). [831707] https://doi.org/10.1117/12.911504

    Automatic corpus callosum segmentation using a deformable active Fourier contour model. / Vachet, Clement; Yvernault, Benjamin; Bhatt, Kshamta; Smith, Rachel G.; Gerig, Guido; Hazlett, Heather Cody; Styner, Martin.

    Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging. Vol. 8317 2012. 831707.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Vachet, C, Yvernault, B, Bhatt, K, Smith, RG, Gerig, G, Hazlett, HC & Styner, M 2012, Automatic corpus callosum segmentation using a deformable active Fourier contour model. in Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging. vol. 8317, 831707, Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging, San Diego, CA, United States, 2/5/12. https://doi.org/10.1117/12.911504
    Vachet C, Yvernault B, Bhatt K, Smith RG, Gerig G, Hazlett HC et al. Automatic corpus callosum segmentation using a deformable active Fourier contour model. In Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging. Vol. 8317. 2012. 831707 https://doi.org/10.1117/12.911504
    Vachet, Clement ; Yvernault, Benjamin ; Bhatt, Kshamta ; Smith, Rachel G. ; Gerig, Guido ; Hazlett, Heather Cody ; Styner, Martin. / Automatic corpus callosum segmentation using a deformable active Fourier contour model. Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging. Vol. 8317 2012.
    @inproceedings{a422256702eb47cdac169a2e11cdf189,
    title = "Automatic corpus callosum segmentation using a deformable active Fourier contour 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.",
    keywords = "Corpus callosum, Fourier coefficient, Segmentation, Shape model",
    author = "Clement Vachet and Benjamin Yvernault and Kshamta Bhatt and Smith, {Rachel G.} and Guido Gerig and Hazlett, {Heather Cody} and Martin Styner",
    year = "2012",
    doi = "10.1117/12.911504",
    language = "English (US)",
    isbn = "9780819489661",
    volume = "8317",
    booktitle = "Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging",

    }

    TY - GEN

    T1 - Automatic corpus callosum segmentation using a deformable active Fourier contour model

    AU - Vachet, Clement

    AU - Yvernault, Benjamin

    AU - Bhatt, Kshamta

    AU - Smith, Rachel G.

    AU - Gerig, Guido

    AU - Hazlett, Heather Cody

    AU - Styner, Martin

    PY - 2012

    Y1 - 2012

    N2 - 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.

    AB - 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.

    KW - Corpus callosum

    KW - Fourier coefficient

    KW - Segmentation

    KW - Shape model

    UR - http://www.scopus.com/inward/record.url?scp=84860758895&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=84860758895&partnerID=8YFLogxK

    U2 - 10.1117/12.911504

    DO - 10.1117/12.911504

    M3 - Conference contribution

    AN - SCOPUS:84860758895

    SN - 9780819489661

    VL - 8317

    BT - Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging

    ER -