# MIMIR: Deep Regression for Automated Analysis of UK Biobank Body MRI

arXiv: https://arxiv.org/abs/2106.11731 · HTML: https://mlsanity.com/paper/2106.11731/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 22 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Large-scale biometry with interpretable neural network regression on UK Biobank body MRI](https://mlsanity.com/paper/2002.06862/) (2020)
- [Kidney segmentation in neck-to-knee body MRI of 40,000 UK Biobank participants](https://mlsanity.com/paper/2006.06996/) (2020)
- [Uncertainty-Aware Body Composition Analysis with Deep Regression Ensembles on UK Biobank MRI](https://mlsanity.com/paper/2101.06963/) (2021)
- [Deep regression for uncertainty-aware and interpretable analysis of large-scale body MRI](https://mlsanity.com/paper/2105.07797/) (2021)

## Built on by (most cited first, 1 of 1)

- [X-Diffusion: Generating Detailed 3D MRI Volumes From a Single Image Using Cross-Sectional Diffusion Models](https://mlsanity.com/paper/2404.19604/) (2024)

## Abstract (arXiv)

UK Biobank (UKB) conducts large-scale examinations of more than half a million volunteers, collecting health-related information on genetics, lifestyle, blood biochemistry, and more. Medical imaging of 100,000 subjects, with 70,000 follow-up sessions, enables measurements of organs, muscle, and body composition. With up to 170,000 mounting MR images, various methodologies are accordingly engaged in large-scale image analysis. This work presents an experimental inference engine that can automatically predict a comprehensive profile of subject metadata from UKB neck-to-knee body MRI. It was evaluated in cross-validation for baseline characteristics such as age, height, weight, and sex, but also measurements of body composition, organ volumes, and abstract properties like grip strength, pulse rate, and type 2 diabetic status. It predicted subsequently released test data covering twelve body composition metrics with a 3% median error. The proposed system can automatically analyze one thousand subjects within ten minutes, providing individual confidence intervals. The underlying methodology utilizes convolutional neural networks for image-based mean-variance regression on two-dimensional representations of the MRI data. This work aims to make the proposed system available for free to researchers, who can use it to obtain fast and fully-automated estimates of 72 different measurements immediately upon release of new UKB image data.