Abstract
Along with rich health-related metadata, medical images have been acquired for over 40,000 male and female UK Biobank participants, aged 44–82, since 2014. Phenotypes derived from these images, such as measurements of body composition from MRI, can reveal new links between genetics, cardiovascular disease, and metabolic conditions. In this work, six measurements of body composition and adipose tissues were automatically estimated by image-based, deep regression with ResNet50 neural networks from neck-to-knee body MRI. Despite the potential for high speed and accuracy, these networks produce no output segmentations that could indicate the reliability of individual measurements. The presented experiments therefore examine uncertainty quantification with mean-variance regression and ensembling to estimate individual measurement errors and thereby identify potential outliers, anomalies, and other failure cases automatically. In 10-fold cross-validation on data of about 8500 subjects, mean-variance regression and ensembling showed complementary benefits, reducing the mean absolute error across all predictions by 12%. Both improved the calibration of uncertainties and their ability to identify high prediction errors. With intra-class correlation coefficients (ICC) above 0.97, all targets except the liver fat content yielded relative measurement errors below 5%. Testing on another 1000 subjects showed consistent performance, and the method was finally deployed for inference to 30,000 subjects with missing reference values. The results indicate that deep regression ensembles could ultimately provide automated, uncertainty-aware measurements of body composition for more than 120,000 UK Biobank neck-to-knee body MRI that are to be acquired within the coming years.
Highlights
UK Biobank studies more than half a million volunteers by collecting data on blood biochemistry, genetics, questionnaires on lifestyle, and medical records (Sudlow et al, 2015).For 100,000 participants, the ongoing examinations include medical imaging, such as dedicated MRI of the brain, heart, liver, pancreas, and the entire body from neck to knee (Littlejohns et al, 2020)
Neural networks have been proposed for segmentation of adipose tissues in other studies involving computed tomography (CT) (Wang et al, 2017; Weston et al, 2019) and MRI (Langner et al, 2019; Estrada et al, 2020; Küstner et al, 2020)
Calibrated uncertainties define confidence intervals that cover, on a set of samples, a percentage of errors that corresponds exactly to their specific confidence level. Both mean-variance regression and ensembling provided comple mentary benefits. Combining both yielded the best predictive perfor mance, shown in Table 1 and Fig. 2, with additional detail provided in the supplementary material
Summary
UK Biobank studies more than half a million volunteers by collecting data on blood biochemistry, genetics, questionnaires on lifestyle, and medical records (Sudlow et al, 2015).For 100,000 participants, the ongoing examinations include medical imaging, such as dedicated MRI of the brain, heart, liver, pancreas, and the entire body from neck to knee (Littlejohns et al, 2020). Recent works proposed fully-automated techniques with neural networks for segmentation, which have been applied to the heart (Bai et al, 2018), kidney (Langner et al, 2020a), pancreas (Basty et al, 2020; Bagur et al, 2020), and liver (Irving et al., 2017), and the iliopsoas muscles (Fitzpatrick et al, 2020), spleen, adipose tissues, and more (Liu et al, 2021). Similar to the latter, neural networks have been proposed for segmentation of adipose tissues in other studies involving computed tomography (CT) (Wang et al, 2017; Weston et al, 2019) and MRI (Langner et al, 2019; Estrada et al, 2020; Küstner et al, 2020)
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