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

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.