Fig. 1: Convolutional neural networks to quantify adipose tissue depots from body MRI images.

a Two-dimensional projections are created by computing the mean pixel intensity along the coronal and sagittal axes. Two images for each participant were used as inputs into the convolutional neural network: one consisting of the coronal and sagittal two-dimensional projections in the fat phase, and another consisting of the same projections in the water phase. b Convolutional neural networks trained on two-dimensional MRI projections achieved near-perfect prediction of each fat depot volume in the holdout set (Supplementary Table 3). c Three female participants with similar BMI (ranging from 29.1 to 29.6 kg/m²) but highly discordant fat depot volumes quantified by convolutional neural networks. Fat depot volume percentiles are computed relative to a subgroup of female participants with overweight BMI (25 ≤ BMI < 30). Note that outlines for each fat depot are drawn as a visual aid for each fat depot and do not reflect segmentation. Abbreviations: VAT, visceral adipose tissue; ASAT, abdominal subcutaneous adipose tissue; GFAT, gluteofemoral adipose tissue.