Organ Traits

Applied a U-Net-based CNN segmentation pipeline to over 38,000 abdominal MRI scans from UK Biobank. The deep learning model automatically segmented 7 organs/tissues (liver, pancreas, kidneys, spleen, lungs, visceral adipose tissue, subcutaneous adipose tissue) and quantified their volume, fat content (via signal intensity), and iron content (via T2* mapping). GWAS on these 11 DL-derived traits identified 93 independent genome-wide significant associations (heritability 8-44%), including 4 novel liver trait associations.

deep learning, abdominal MRI segmentation, U-Net, organ volume quantification, liver fat, pancreas iron, UK Biobank

Cardiometabolic diseases are an increasing global health burden. While socioeconomic, environmental, behavioural, and genetic risk factors have been identified, a better understanding of the underlying mechanisms is required to develop more effective interventions. Magnetic resonance imaging (MRI) has been used to assess organ health, but biobank-scale studies are still in their infancy. Using over 38,000 abdominal MRI scans in the UK Biobank, we used deep learning to quantify volume, fat, and iron in seven organs and tissues, and demonstrate that imaging-derived phenotypes reflect health status. We identify 93 independent genome-wide significant associations.