Uncertainty-aware body composition analysis with deep regression ensembles on UK Biobank MRI

Voxel-wise body composition analysis using image registration of a three-slice CT imaging protocol: methodology and proof-of-concept studies

BACKGROUND

Computed tomography (CT) is an imaging modality commonly used for studies of internal body structures and very useful for detailed studies of body composition. The aim of this study was to develop and evaluate a fully automatic image registration framework for inter-subject CT slice registration. The aim was also to use the results, in a set of proof-of-concept studies, for voxel-wise statistical body composition analysis (Imiomics) of correlations between imaging and non-imaging data.

METHODS

The current study utilized three single-slice CT images of the liver, abdomen, and thigh from two large cohort studies, SCAPIS and IGT. The image registration method developed and evaluated used both CT images together with image-derived tissue and organ segmentation masks. To evaluate the performance of the registration method, a set of baseline 3-single-slice CT images (from 2780 subjects including 8285 slices) from the SCAPIS and IGT cohorts were registered. Vector magnitude and intensity magnitude error indicating inverse consistency were used for evaluation. Image registration results were further used for voxel-wise analysis of associations between the CT images (as represented by tissue volume from Hounsfield unit and Jacobian determinant) and various explicit measurements of various tissues, fat depots, and organs collected in both cohort studies.

RESULTS

Our findings demonstrated that the key organs and anatomical structures were registered appropriately. The evaluation parameters of inverse consistency, such as vector magnitude and intensity magnitude error, were on average less than 3 mm and 50 Hounsfield units. The registration followed by Imiomics analysis enabled the examination of associations between various explicit measurements (liver, spleen, abdominal muscle, visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), thigh SAT, intermuscular adipose tissue (IMAT), and thigh muscle) and the voxel-wise image information.

CONCLUSION

The developed and evaluated framework allows accurate image registrations of the collected three single-slice CT images and enables detailed voxel-wise studies of associations between body composition and associated diseases and risk factors.

A review of the application of deep learning in obesity: From early prediction aid to advanced management assistance

BACKGROUND AND AIMS

Obesity is a chronic disease which can cause severe metabolic disorders. Machine learning (ML) techniques, especially deep learning (DL), have proven to be useful in obesity research. However, there is a dearth of systematic reviews of DL applications in obesity. This article aims to summarize the current trend of DL usage in obesity research.

METHODS

An extensive literature review was carried out across multiple databases, including PubMed, Embase, Web of Science, Scopus, and Medline, to collate relevant studies published from January 2018 to September 2023. The focus was on research detailing the application of DL in the context of obesity. We have distilled critical insights pertaining to the utilized learning models, encompassing aspects of their development, principal results, and foundational methodologies.

RESULTS

Our analysis culminated in the synthesis of new knowledge regarding the application of DL in the context of obesity. Finally, 40 research articles were included. The final collection of these research can be divided into three categories: obesity prediction (n = 16); obesity management (n = 13); and body fat estimation (n = 11).

CONCLUSIONS

This is the first review to examine DL applications in obesity. It reveals DL's superiority in obesity prediction over traditional ML methods, showing promise for multi-omics research. DL also innovates in obesity management through diet, fitness, and environmental analyses. Additionally, DL improves body fat estimation, offering affordable and precise monitoring tools. The study is registered with PROSPERO (ID: CRD42023475159).

Abdominal fat quantification using convolutional networks

OBJECTIVES

To present software for automated adipose tissue quantification of abdominal magnetic resonance imaging (MRI) data using fully convolutional networks (FCN) and to evaluate its overall performance-accuracy, reliability, processing effort, and time-in comparison with an interactive reference method.

MATERIALS AND METHODS

Single-center data of patients with obesity were analyzed retrospectively with institutional review board approval. Ground truth for subcutaneous (SAT) and visceral adipose tissue (VAT) segmentation was provided by semiautomated region-of-interest (ROI) histogram thresholding of 331 full abdominal image series. Automated analyses were implemented using UNet-based FCN architectures and data augmentation techniques. Cross-validation was performed on hold-out data using standard similarity and error measures.

RESULTS

The FCN models reached Dice coefficients of up to 0.954 for SAT and 0.889 for VAT segmentation during cross-validation. Volumetric SAT (VAT) assessment resulted in a Pearson correlation coefficient of 0.999 (0.997), relative bias of 0.7% (0.8%), and standard deviation of 1.2% (3.1%). Intraclass correlation (coefficient of variation) within the same cohort was 0.999 (1.4%) for SAT and 0.996 (3.1%) for VAT.

CONCLUSION

The presented methods for automated adipose-tissue quantification showed substantial improvements over common semiautomated approaches (no reader dependence, less effort) and thus provide a promising option for adipose tissue quantification.

CLINICAL RELEVANCE STATEMENT

Deep learning techniques will likely enable image-based body composition analyses on a routine basis. The presented fully convolutional network models are well suited for full abdominopelvic adipose tissue quantification in patients with obesity.

KEY POINTS

• This work compared the performance of different deep-learning approaches for adipose tissue quantification in patients with obesity. • Supervised deep learning-based methods using fully convolutional networks  were suited best. • Measures of accuracy were equal to or better than the operator-driven approach.

Diet composition, nutrient substitutions and circulating fatty acids in relation to ectopic and visceral fat depots

BACKGROUND & AIMS

Short-term randomized trials have demonstrated that replacing saturated fat (SFA) with polyunsaturated fat (PUFA) causes a reduction or prevention of liver fat accumulation, but population-based studies on diet and body fat distribution are limited. We investigated cross-sectional associations between diet, circulating fatty acids and liver fat, visceral adipose tissue (VAT), intermuscular adipose tissue (IMAT) and other fat depots using different energy-adjustment models.

METHODS

Sex-stratified analyses of n = 9119 (for serum fatty acids) to 13 849 (for nutrients) participants in UK Biobank were conducted. Fat depots were assessed by MRI, circulating fatty acids by NMR spectroscopy and diet by repeated 24-h recalls. Liver fat, VAT and IMAT were primary outcomes; total adipose tissue (TAT) and abdominal subcutaneous adipose tissue (ASAT) were secondary outcomes. Three a priori defined models were constructed: the all-components model, standard model and leave-one-out model (main model including specified nutrient substitutions). Imiomics (MRI-derived) was used to confirm and visualize associations.

RESULTS

In women, substituting carbohydrates and free sugars with saturated fat (SFA) was positively associated with liver fat (β (95% CI) = 0.19 (0.02, 0.36) and β (95% CI) = 0.20 (0.05-0.35), respectively) and IMAT (β (95% CI) = 0.07 (0.00, 0.14) and β (95% CI) = 0.08 (0.02, 0.13), respectively), whereas substituting animal fat with plant fat was inversely associated with IMAT, ASAT and TAT. In the all-components and standard models, SFA and animal fat were positively associated with liver fat, IMAT and VAT whereas plant fat was inversely associated with IMAT in women. Few associations were observed in men. Circulating polyunsaturated fatty acids (PUFA) were inversely associated with liver fat, IMAT and VAT in both men and women, whereas SFA and monounsaturated fatty acids were positively associated.

CONCLUSIONS

Type of dietary fat may be an important determinant of ectopic fat in humans consuming their habitual diet. Plant fat and PUFA should be preferred over animal fat and SFA. This is corroborated by circulating fatty acids and overall consistent through different energy adjustment models.

TWITTER SUMMARY

In UK Biobank, intake of saturated- and animal fat were positively whereas biomarkers of polyunsaturated fat were inversely associated with liver-, visceral- and intermuscular fat. Type of dietary fat may be a determinant of ectopic fat, a risk factor for cardiometabolic disease.

MIMIR: Deep Regression for Automated Analysis of UK Biobank MRI Scans

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. 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. This study presents an experimental inference engine for automated analysis of UKB neck-to-knee body 1.5-T MRI scans. 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. Prediction intervals for each end point were generated based on uncertainty quantification. 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. The processing of MRI scans from 1000 participants required 10 minutes. The underlying method used convolutional neural networks for image-based mean-variance regression on two-dimensional representations of the MRI data. An implementation was made publicly available for fast and fully automated estimation of 72 different measurements from future releases of UKB image data. Keywords: MRI, Adipose Tissue, Obesity, Metabolic Disorders, Volume Analysis, Whole-Body Imaging, Quantification, Supervised Learning, Convolutional Neural Network (CNN) © RSNA, 2022.