  Purpose: Body composition measurements from routine abdominal CT can yield
personalized risk assessments for asymptomatic and diseased patients. In
particular, attenuation and volume measures of muscle and fat are associated
with important clinical outcomes, such as cardiovascular events, fractures, and
death. This study evaluates the reliability of an Internal tool for the
segmentation of muscle and fat (subcutaneous and visceral) as compared to the
well-established public TotalSegmentator tool.
  Methods: We assessed the tools across 900 CT series from the publicly
available SAROS dataset, focusing on muscle, subcutaneous fat, and visceral
fat. The Dice score was employed to assess accuracy in subcutaneous fat and
muscle segmentation. Due to the lack of ground truth segmentations for visceral
fat, Cohen's Kappa was utilized to assess segmentation agreement between the
tools.
  Results: Our Internal tool achieved a 3% higher Dice (83.8 vs. 80.8) for
subcutaneous fat and a 5% improvement (87.6 vs. 83.2) for muscle segmentation
respectively. A Wilcoxon signed-rank test revealed that our results were
statistically different with p<0.01. For visceral fat, the Cohen's kappa score
of 0.856 indicated near-perfect agreement between the two tools. Our internal
tool also showed very strong correlations for muscle volume (R^2=0.99), muscle
attenuation (R^2=0.93), and subcutaneous fat volume (R^2=0.99) with a moderate
correlation for subcutaneous fat attenuation (R^2=0.45).
  Conclusion: Our findings indicated that our Internal tool outperformed
TotalSegmentator in measuring subcutaneous fat and muscle. The high Cohen's
Kappa score for visceral fat suggests a reliable level of agreement between the
two tools. These results demonstrate the potential of our tool in advancing the
accuracy of body composition analysis.
