Body composition analysis provides essential evidence for evaluating patient health, predicting disease risk, and guiding personalized treatment. Abdominal computed tomography (CT) enables high-resolution visualization of skeletal muscle, adipose tissue, and major organs, making it well-suited for constructing 3D anatomical distribution maps. However, current clinical practice largely depends on manual analysis, which is limited in scope, efficiency, and standardization. This review presents recent deep learning techniques, focusing on methods developed by our group, that aim to automate the generation of anatomical distribution maps from abdominal CT. The key topics include: (1) automated localization and identification of the entire spine beyond single-level detection at the third lumbar vertebra (L3); (2) cross-domain consistency learning to overcome limited annotations in medical imaging; (3) multi-organ segmentation that incorporates inter-slice structural continuity and anatomical relationships; and (4) a comparative study of continual learning and multi-dataset learning for building generalizable models. These approaches enable rapid, standardized generation of anatomical distribution maps across large-scale datasets and diverse patient cohorts, providing a robust technical foundation for future precision medicine applications. In particular, these methods are expected to provide for early cancer detection, pediatric CT analysis, and clinical correlation studies linking anatomical distribution maps with outcomes such as cancer prognosis.