An Automated and Robust Framework for Quantification of Muscle and Fat in the Thigh

The tissue quantification in the thigh (e.g. cross-sectional areas of adipose tissue and muscle) is important, since their quantities reflect adverse metabolic effects and muscle function. Traditional manual analysis is time-consuming and operator-dependent, especially in the case of multi-slices or 3D datasets. In clinical trials, there are a large amount of datasets acquired from magnetic resonance imaging (MRI) or X-ray computed tomography (CT) that requires automatic labeling of individual tissues. Since most segmentation algorithms are not suited for different modalities, we present an automatic and robust framework for the quantitative assessment of muscle and fat tissues on 3D MR or CT data. In our framework, a variational Bayesian Gaussian mixture model is used to cluster regions of interest in images into adipose tissues (fat and marrow), muscle, bone and background. The identification of each cluster is based on marrow detection. Furthermore, we use a combination of parametric and geodesic active contour models to distinguish different adipose tissues in 3D images. To validate our proposed framework, we have conducted preliminary experiments on five volumetric mid-thigh axial datasets of MR and CT images from clinical trials.