Artificial intelligence based automatic quantification of epicardial adipose tissue suitable for large scale population studies

Figures

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  Figure 1. Basic design of the two CNN models which work in series. EAT-Net outputs segmentations of the heart, from which epicardial adipose tissue (EAT) volume, and EAT attenuation values are calculated. Crop-Net outputs an estimation of the fraction of EAT volume that is missing in incomplete image sets.

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  Figure 2. Bland–Altman plot showing the agreement between epicardial adipose tissue volumes (EATV) derived from automatic segmentations by the software and manual ground truth. The black dotted line represents the mean difference between the methods (− 1.76 ml), and the red dashed lines represent the limits of agreement.

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  Figure 3. The effect of image noise on voxel classification. (a) An axial slice after application of the largest connected volume filter and thresholding (− 190 to − 30 HU) but prior to application of the anatomical noise filter. Epicardial adipose tissue (EAT) is represented in yellow. It is evident that the chambers of the heart contain a number of voxels wrongly classified as EAT. (b) The same axial slice, but after also applying the anatomical noise-filter. Here true EAT is represented in green, while voxels removed by the filter are blue. Voxels in red are not affected by the filter. It can be seen that most of the voxels misclassified by thresholding are correctly identified by the filter.

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  Figure 4. Examples of automatic segmentations. Epicardial adipose tissue (EAT) is colored blue, non-EAT within the pericardium is colored green. Each row, from left to right, represents an inferior, mid- and superior axial slice from the same individual. Imperfections in the automatic segmentations are noted with red arrows. As we can see, the last case shows some slight areas of missing EAT in the most inferior and superior parts respectively. At whole-heart level, when measuring the total EATV, these errors were found to be insignificant.

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  Figure 5. Examples of failed segmentations found in the 1400 cases that were visually evaluated. Epicardial adipose tissue (EAT) is colored blue, non-EAT within the pericardium is colored green. The most significant segmentation errors are marked with red arrows. The correct pericardial contour is marked with a dashed red line. Each row, from left to right, represents a mid-axial and a mid-sagittal (side-view) slice from the same individual. A large hiatal hernia located posterior to the heart, the probable reason for failure in the first of the two cases, is marked with an asterisk (*). In the second case the reason for segmentation failure seems to be a left-sided breast implant, marked with two asterisks (**).

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  Figure 6. Epicardial adipose tissue (EAT) volume and attenuation and their association with anthropometrics and cardiometabolic risk factors. Using a random forest classifier, EAT volume (a) and attenuation (c) was predicted from a set of 18 anthropometric measures and cardiometabolic risk factors. Total explained variance was calculated from the model's prediction. Variable importance (b and d) was calculated from the increase in the mean squared error caused by permutation of each variable.