In: 2016 IEEE Conference on Computer Vision and Pattern Recognition Deep Residual Learning for Image Recognition. Algorithms for the capture and adjudication of prevalent and Eastwood SV, Mathur R, Atkinson M, et al. Comput Med Imaging and Graph, 2021 93 : 101994. Uncertainty-aware body composition analysis with deep regressionĮnsembles on UK Biobank MRI. Langner T, Gustafsson FK, Avelin B, Strand R, Ahlström H, Kullberg J. Recognition Workshops (CVPRW), Seattle, WA, June 14–19, 2020. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Evaluating Scalable Bayesian Deep Learning Methods for RobustĬomputer Vision. Simple and Scalable Predictive Uncertainty Estimation using DeepĮnsembles. Lakshminarayanan B, Pritzel A, Blundell C. Brain age prediction using deep learning uncovers associated Jónsson BA, Bjornsdottir G, Thorgeirsson TE, et al. Deep regression for uncertainty-aware and interpretable analysis Langner T, Strand R, Ahlström H, Kullberg J. UK Biobank and German National Cohort Magnetic Resonance Imaging Deep Learning-Based Automated Abdominal Organ Segmentation in the Genetic architecture of 11 organ traits derived from abdominal Kidney segmentation in neck-to-knee body MRI of 40,000 UK Biobank Communications inĬomputer and Information Science, vol 1248. Medical Image Understanding and Analysis. In: Papież BW, Namburete AIL, Yaqub M, Noble JA, eds. Pancreas Segmentation-Derived Biomarkers: Volume and Shape Bagur AT, Ridgway G, McGonigle J, Brady SM, Bulte D. In: 2020 IEEE 17th International Symposium on Biomedical Imaging Using Multi-Echo and T1-Weighted MRI Data. Automated Measurement of Pancreatic Fat and Iron Concentration Basty N, Liu Y, Cule M, Thomas EL, Bell JD, Whitcher B. Communications inĬomputer and Information Science, vol 723. In: Valdés Hernández M, González-Castro V eds. Deep Quantitative Liver Segmentation and Vessel Exclusion toĪssist in Liver Assessment. Body Composition Profiling in the UK Biobank Imaging ![]() Crossref, Medline, Google Scholar Characterisation of liver fat in the UK BiobankĬohort. Feasibility of MR-Based Body Composition Analysis in Large Scale West J, Dahlqvist Leinhard O, Romu T, et al. Rationale, data collection, management and future directions. The UK Biobank imaging enhancement of 100,000 participants: Littlejohns TJ, Holliday J, Gibson LM, et al. This article was corrected on July 7, 2022. Keywords: MRI, Adipose Tissue, Obesity, Metabolic Disorders, Volume Analysis, Whole-Body Imaging, Quantification, Supervised Learning, Convolutional Neural Network (CNN)Īn earlier incorrect version appeared online. An implementation was made publicly available for fast and fully automated estimation of 72 different measurements from future releases of UKB image data. The underlying method used convolutional neural networks for image-based mean-variance regression on two-dimensional representations of the MRI data. The processing of MRI scans from 1000 participants required 10 minutes. On a subsequent release of UKB data, the proposed method predicted 12 body composition metrics with a 3% median error and yielded mostly well-calibrated individual prediction intervals. Prediction intervals for each end point were generated based on uncertainty quantification. ![]() This retrospective cross-validation study includes data from 38 916 participants (52% female mean age, 64 years) to capture baseline characteristics, such as age, height, weight, and sex, as well as measurements of body composition, organ volumes, and abstract properties, such as grip strength, pulse rate, and type 2 diabetes status. This study presents an experimental inference engine for automated analysis of UKB neck-to-knee body 1.5-T MRI scans. Ongoing medical imaging of 100 000 participants with 70 000 follow-up sessions will yield up to 170 000 MRI scans, enabling image analysis of body composition, organs, and muscle. UK Biobank (UKB) has recruited more than 500 000 volunteers from the United Kingdom, collecting health-related information on genetics, lifestyle, blood biochemistry, and more.
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