Body composition is an informative biomarker in the treatment of cancer. In particular, low muscle mass has been associated with higher chemotherapy toxicity, shorter time to tumor progression, poorer surgical outcomes, impaired functional status, and shorter survival. However, because CT-based body composition assessment requires outlining the different tissues in the image, which is timeconsuming, its practical value is currently limited. To form an estimate of body composition, different tissues are often segmented manually in a single 2D slice from the abdomen.
For use in both routine care and in research studies, automatic segmentation of the different tissue types in the abdomen is desirable.
This study focuses on the development and testing of an automatic approach to segment muscle and fat tissue in the entire abdomen. The four classes of interest are skeletal muscle (SM), inter-muscular adipose tissue (IMAT), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT). A deep neural network is trained on two-dimensional CT slices at the level of the third lumbar vertebra. Three experiments were carried out with the goal of improving the network with information from other, unannotated data sources. Active learning methods were applied to sample additional data to annotate and include in the training of the model. The proposed algorithm combines two models to segment muscle and fat in the entire abdomen and achieves state-of-the-art results. Dice scores of 0.91, 0.84, 0.97, and 0.97 were attained for SM, IMAT, VAT, and SAT, respectively, averaged over five locations throughout the abdomen.