Subcortical structure segmentation using probabilistic atlas priors

Sylvain Gouttard, Martin Styner, Sarang Joshi, Rachel G. Smith, Heather Cody Hazlett, Guido Gerig

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

    Abstract

    The segmentation of the subcortical structures of the brain is required for many forms of quantitative neuroanatomy analysis. The volumetric and shape parameters of structures such as lateral ventricles, putamen, caudate, hippocampus, pallidus and amygdala are employed to characterize a disease or its evolution. This paper presents a fully automatic segmentation of these structures via a non-rigid registration of a probabilistic atlas prior and alongside a comprehensive validation. Our approach is based on an unbiased diffeomorphic atlas with probabilistic spatial priors built from a training set of MR images with corresponding manual segmentations. The atlas building computes an average image along with transformation fields mapping each training case to the average image. These transformation fields are applied to the manually segmented structures of each case in order to obtain a probabilistic map on the atlas. When applying the atlas for automatic structural segmentation, an MR image is first intensity inhomogeneity corrected, skull stripped and intensity calibrated to the atlas. Then the atlas image is registered to the image using an affine followed by a deformable registration matching the gray level intensity. Finally, the registration transformation is applied to the probabilistic maps of each structures, which are then thresholded at 0.5 probability. Using manual segmentations for comparison, measures of volumetric differences show high correlation with our results. Furthermore, the dice coefficient, which quantifies the volumetric overlap, is higher than 62% for all structures and is close to 80% for basal ganglia. The intraclass correlation coefficient computed on these same datasets shows a good inter-method correlation of the volumetric measurements. Using a dataset of a single patient scanned 10 times on 5 different scanners, reliability is shown with a coefficient of variance of less than 2 percents over the whole dataset. Overall, these validation and reliability studies show that our method accurately and reliably segments almost all structures. Only the hippocampus and amygdala segmentations exhibit relative low correlation with the manual segmentation in at least one of the validation studies, whereas they still show appropriate dice overlap coefficients.

    Original languageEnglish (US)
    Title of host publicationMedical Imaging 2007
    Subtitle of host publicationImage Processing
    EditionPART 2
    DOIs
    StatePublished - 2007
    EventMedical Imaging 2007: Image Processing - San Diego, CA, United States
    Duration: Feb 18 2007Feb 20 2007

    Publication series

    NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
    NumberPART 2
    Volume6512
    ISSN (Print)1605-7422

    Other

    OtherMedical Imaging 2007: Image Processing
    CountryUnited States
    CitySan Diego, CA
    Period2/18/072/20/07

    Keywords

    • Segmentation
    • Shape
    • Shape analysis
    • Validation

    ASJC Scopus subject areas

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

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  • Cite this

    Gouttard, S., Styner, M., Joshi, S., Smith, R. G., Hazlett, H. C., & Gerig, G. (2007). Subcortical structure segmentation using probabilistic atlas priors. In Medical Imaging 2007: Image Processing (PART 2 ed.). [65122J] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 6512, No. PART 2). https://doi.org/10.1117/12.708626