Keywords: computed tomography, segmentation, body composition, abdominal CT
TL;DR: We present Comp2Comp, an open-source Python package for rapid and automated body composition analysis of CT scans.
Abstract: Computed tomography (CT) can provide quantitative body composition metrics of tissue volume, morphology, and quality which are valuable for disease prediction and prognostication. However, manually extracting these measures is a cumbersome and time-consuming task. Proprietary software to automate this process exist, but these software are closed-source, impeding large-scale access to and usage of these tools. To address this, we have built Comp2Comp, an open-source Python package for rapid and automated body composition analysis of CT scans. The primary advantages of Comp2Comp are its open-source nature, the inclusion of multiple tissue analysis capabilities within a single package, and its extensible design. We discuss the architecture of Comp2Comp and report initial validation results. Comp2Comp can be found at https://github.com/StanfordMIMI/Comp2Comp.