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.