Kidney segmentation in neck-to-knee body MRI of 40,000 UK Biobank participants

TL;DR

This paper presents an automated neural network-based pipeline for kidney segmentation in large-scale UK Biobank MRI data, enabling efficient volume measurement of thousands of subjects for health research.

Contribution

A novel automated segmentation method for kidneys in neck-to-knee MRI, achieving high accuracy and scalability for large datasets like UK Biobank.

Findings

Relative error of 3.8% in segmentation accuracy

Dice score of 0.956 indicating high overlap with manual segmentation

Processed 40,000 MRI scans within two days

Abstract

The UK Biobank is collecting extensive data on health-related characteristics of over half a million volunteers. The biological samples of blood and urine can provide valuable insight on kidney function, with important links to cardiovascular and metabolic health. Further information on kidney anatomy could be obtained by medical imaging. In contrast to the brain, heart, liver, and pancreas, no dedicated Magnetic Resonance Imaging (MRI) is planned for the kidneys. An image-based assessment is nonetheless feasible in the neck-to-knee body MRI intended for abdominal body composition analysis, which also covers the kidneys. In this work, a pipeline for automated segmentation of parenchymal kidney volume in UK Biobank neck-to-knee body MRI is proposed. The underlying neural network reaches a relative error of 3.8%, with Dice score 0.956 in validation on 64 subjects, close to the 2.6% and Dice score 0.962 for repeated segmentation by one human operator. The released MRI of about 40,000 subjects can be processed within two days, yielding volume measurements of left and right kidney. Algorithmic quality ratings enabled the exclusion of outliers and potential failure cases. The resulting measurements can be studied and shared for large-scale investigation of associations and longitudinal changes in parenchymal kidney volume.