  Body tissue composition is a long-known biomarker with high diagnostic and
prognostic value in cardiovascular, oncological and orthopaedic diseases, but
also in rehabilitation medicine or drug dosage. In this study, the aim was to
develop a fully automated, reproducible and quantitative 3D volumetry of body
tissue composition from standard CT examinations of the abdomen in order to be
able to offer such valuable biomarkers as part of routine clinical imaging.
Therefore an in-house dataset of 40 CTs for training and 10 CTs for testing
were fully annotated on every fifth axial slice with five different semantic
body regions: abdominal cavity, bones, muscle, subcutaneous tissue, and
thoracic cavity. Multi-resolution U-Net 3D neural networks were employed for
segmenting these body regions, followed by subclassifying adipose tissue and
muscle using known hounsfield unit limits. The S{\o}rensen Dice scores averaged
over all semantic regions was 0.9553 and the intra-class correlation
coefficients for subclassified tissues were above 0.99. Our results show that
fully-automated body composition analysis on routine CT imaging can provide
stable biomarkers across the whole abdomen and not just on L3 slices, which is
historically the reference location for analysing body composition in the
clinical routine.
