  Psoas muscle measurements are frequently used as markers of sarcopenia and
predictors of health. Manually measured cross-sectional areas are most commonly
used, but there is a lack of consistency regarding the position of the
measurementand manual annotations are not practical for large population
studies. We have developed a fully automated method to measure iliopsoas muscle
volume (comprised of the psoas and iliacus muscles) using a convolutional
neural network. Magnetic resonance images were obtained from the UK Biobank for
5,000 male and female participants, balanced for age, gender and BMI. Ninety
manual annotations were available for model training and validation. The model
showed excellent performance against out-of-sample data (dice score coefficient
of 0.912 +/- 0.018). Iliopsoas muscle volumes were successfully measured in all
5,000 participants. Iliopsoas volume was greater in male compared with female
subjects. There was a small but significant asymmetry between left and right
iliopsoas muscle volumes. We also found that iliopsoas volume was significantly
related to height, BMI and age, and that there was an acceleration in muscle
volume decrease in men with age. Our method provides a robust technique for
measuring iliopsoas muscle volume that can be applied to large cohorts.
