Abstract Body composition metrics are essential for assessing patient condition, particularly in the context of sarcopenia, but are often underutilized in clinical practice due to the complexity of image-based analysis. This work presents a modular, Docker-based framework for the automated extraction of sarcopenia-relevant parameters from single-slice CT images at the L3 level. A deep learning model performs multilabel segmentation of key muscle and adipose tissues, including intramuscular adipose tissue (IMAT), enabling the calculation of clinically relevant metrics such as cross-sectional area and tissue density. The system supports both conventional and photon-counting CT data and includes a streamlined graphical user interface designed for ease of use in clinical environments. Results are accessible via a lightweight interface and can be exported in standardized formats for further analysis. The framework is openly available on GitHub and provides a reproducible, secure, and adaptable solution for integrating AI-driven body composition analysis into clinical workflows.
Kreher et al. (Mon,) studied this question.
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