Automatic abdominal fat assessment in obese mice using a segmental shape model

Authors

  • Yang Tang PhD,

    1. Department of Radiology, Children's Hospital Los Angeles, University of Southern California, Los Angeles, California, USA
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  • Priyank Sharma BS,

    1. Department of Radiology, Children's Hospital Los Angeles, University of Southern California, Los Angeles, California, USA
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  • Marvin D. Nelson MD,

    1. Department of Radiology, Children's Hospital Los Angeles, University of Southern California, Los Angeles, California, USA
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  • Richard Simerly PhD,

    1. Department of Paediatrics and Biology, Children's Hospital Los Angeles, University of Southern California, Los Angeles, California, USA
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  • Rex A. Moats PhD

    Corresponding author
    1. Department of Radiology, Children's Hospital Los Angeles, University of Southern California, Los Angeles, California, USA
    • MS#81, Department of Radiology, Children's Hospital Los Angeles, 4650 Sunset Blvd., Los Angeles, CA 90027
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Abstract

Purpose:

To develop a computerized image analysis method to assess the quantity and distribution of abdominal fat tissues in an obese (ob/ob) mouse model relevant to 7 T magnetic resonance imaging (MRI).

Materials and Methods:

A novel segmental shape model is presented that separates visceral adipose tissue (VAT) from subcutaneous adipose tissue (SAT). With shape and distance constraints, it deforms a contour inwards from the skin to the muscle wall and separates the connecting adipose tissues in an ob/ob mouse. The fat tissues are segmented by the adaptive fuzzy C means method to compensate for intensity variation in adipose images. The results were obtained by logical operations applied on the extracted fat images and the separated adipose masks.

Results:

The method was validated by manual segmentations on 109 axial slice images from 7 ob/ob mice. The average correlation coefficients of measured sizes between the automatic and manual results for total adipose tissue (TAT) is 0.907; SAT is 0.944; VAT is 0. 950. The average Dice coefficient of their positions for TAT is 0.941, SAT is 0.935, and VAT is 0.920.

Conclusion:

The automated results correlate well with manual segmentations and the method can be used to increase laboratory automation. J. Magn. Reson. Imaging 2011;. © 2011 Wiley-Liss, Inc.

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