Large-scale biometry with interpretable neural network regression on UK Biobank body MRI
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The UK Biobank study has successfully imaged more than 32,000 volunteer participants with neck-to-knee body MRI. Each scan is linked to extensive metadata, providing a comprehensive survey of imaged anatomy and related health states. Despite its potential for research, this vast amount of data presents a challenge to established methods of evaluation, which often rely on manual input. To date, the range of reference values for cardiovascular and metabolic risk factors is therefore incomplete. In this work, neural networks were trained for regression to infer various biological metrics from the neck-to-knee body MRI automatically. The approach requires no manual intervention or ground truth segmentations for training. The examined fields span 64 variables derived from anthropometric measurements, dual-energy X-ray absorptiometry (DXA), atlas-based segmentations, and dedicated liver scans. The standardized framework achieved a close fit to the target values (median R^2 > 0.97) in 7-fold cross-validation with the ResNet50. Interpretation of aggregated saliency maps suggests that the network correctly targets specific body regions and limbs, and learned to emulate different modalities. On several body composition metrics, the quality of the predictions is within the range of variability observed between established gold standard techniques.
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