Large-Scale Analysis of Iliopsoas Muscle Volumes in the UK Biobank

TL;DR

This study introduces an automated convolutional neural network method to measure iliopsoas muscle volume from MRI scans, enabling large-scale analysis of muscle health markers in the UK Biobank.

Contribution

We developed and validated a fully automated CNN-based approach for measuring iliopsoas muscle volume in large population datasets.

Findings

Iliopsoas volume is higher in males than females.

Significant asymmetry exists between left and right muscles.

Muscle volume correlates with height, BMI, and age, with volume decreasing faster in aging men.

Abstract

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.