UCSF

Micro-computed tomography can be used to provide a precise in-vivo assessment of adipose tissue quantity and distribution, including information on subcutaneous and visceral fat volume in mouse models. This study aims to develop methods to aid edge detection in order to eventually segment out the visceral and subcutaneous fat compartments automatically. The algorithm detailed in this paper optimizes steps in the Canny edge detection method and utilizes low-pass filtering and gradient edge detection. Ten mice (weight range: 19.96 - 57.66 g) were tested with micro-CT scans to verify the utility of this algorithm. The algorithm demonstrated stability despite the broad range of body weights and adiposity. Comparisons of the data between unfiltered versus filtered mice volumes suggest that this algorithm can be used to effectively increase edge strength for use in separating visceral and subcutaneous fat compartments. The eventual application of this method would be to assess metabolic disease risk, such as those associated with central obesity including diabetes, hypertension, and heart disease.