## 5 Direct EAT Segmentation Methods
### Deep Learning-Based Methods  
Similar to machine learning, deep learning is fitting for direct EAT segmentation since it can find very complex shapes in the image. However, deep learning does not require feature selection and reduces the need for heavy image preprocessing. Deep learning models for direct EAT segmentation are most commonly based on U-Net. For instance, [PERSON] et al. [38] used a U-Net-based network to segment EAT and paracardial fat. The U-Net is modified to include an additional pyramid pooling layer. The authors use the same dataset used in [PERSON] et al. [20] but apply data augmentation to artificially increase the amount of data. EAT and background classes are imbalanced, i.e., the number of EAT pixels is small compared to the number of background pixels. This imbalance can cause problems when training CNNs. To get around this problem, the authors use focal loss, which increases the weight of getting the less-populated class wrong. The authors report an area under the ROC curve (AUC) of 0.87 for EAT. The authors do not report a Dice coefficient so it is hard to determine the accuracy of their method, especially since AUC can misrepresent the effectiveness. As opposed to AUC, the Dice coefficient does not consider true negatives (the background class), which, in this case, represents a more accurate measurement than AUC.  
A similar approach is presented in [PERSON] et al. [49], where the authors preprocess the input CT scans using the polar transform. The authors first train a stacked hourglass neural network to predict the optimal origin for the polar transform and then use that network to transform each input image. They then train a second U-Net-based segmentation network on the polar-transformed images. They achieve a relatively low Dice score of 0.78. This result is better than a U-Net baseline on the same dataset but worse than other neural network-based methods [36,37]. However, the authors comment that the results could be further improved by not segmenting EAT directly but instead first segmenting the pericardium. The results indicate that researching the polar transform and other forms of pre-processing could lead to a notable improvement in EAT segmentation results.  
Most direct EAT segmentation methods work on 2D images, with the notable exception of [PERSON] et al. [37]. They use the U-Net architecture on 3D patches of CT images. The model follows a modified U-Net architecture called deep attention U-Net, which includes attention gates. Since the U-Net has both coarse and fine-grained information, it can be modified to include attention gates that use coarse-grained features to suppress non-salient connections in the network, thus removing noisy data from the training process. The authors report a Dice coefficient of 0.85, cross-validated on 40 patients. When compared to results of direct 2D segmentation with a simple U-Net from [PERSON] et al. [36], the results from [PERSON] et al. [37] achieve a better Dice coefficient, which indicates that training on 3D data could be beneficial. After all, EAT is a 3D structure so there could be a lot of mutual information on EAT between two consecutive slices.