Body composition imaging relies on assessment of tissues composition and distribution. Quantitative data of body composition have been linked to pathogenesis and clinical outcomes of a wide spectrum of diseases, including oncological and cardiovascular. Obesity classification is based on body mass index (BMI) which has the shortcoming not to provide any information on the distribution of adipose tissue and skeletal muscle tissue, nor does it allow to distinguish the two main compartments of abdominal adipose tissue: visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). Mounting evidence in recent years evaluated the automated abdominal adipose tissue segmentation on CT and MRI scans by means of machine learning and deep learning algorithms. On this respect, the Dice Score is a common metric to assess the spatial overlap between the predicted label maps and the ground truth. It provides both size and localization consensus for any type of method, not only AI.