## 5 Direct EAT Segmentation Methods
### Machine Learning-Based Methods  
There is a notable trend in using machine learning to fully automatically segment EAT directly. The most common approach is to heavily preprocess the images and then extract various features to feed into a random forest classifier or a neural network. An example of such as approach is present in [PERSON] et al. [45]. The input images are thresholded to the adipose tissue range and then translated to a standardized position with atlas-based segmentation of the retrosternal area. A selection of features is then extracted from the image. These features include the center of gravity of the image, x and y positions, and the gray level of the pixel, as well as additional features from the surrounding neighborhood of each pixel. Using these features as inputs, the authors train a machine learning model based on the random forest classifier to segment each pixel into one of three classes: EAT, paracardial fat, or pericardium. For EAT segmentation, the authors report a Dice score of 0.979 using the dataset from [PERSON] et al. [20]. Similar approaches can be seen in [PERSON] et al. [20] and [PERSON] et al. [46].  
[PERSON] et al. [46] use contrast limited adaptive histogram equalization in addition to thresholding to preprocess the images. Their features also include a Gabor filter bank, a collection of Gabor filters at different scales and rotations. Instead of relying on only a random forest classifier, the features are used to train an ensemble of different classifiers. They achieve a Dice coefficient of 0.94, which indicates that selected features are less salient than those selected in [PERSON] et al. [20,45].  
Other works use different types of optimization algorithms to segment the image. For instance, in [PERSON] and Sudha [47], EAT and paracardial adipose tissue are segmented using region growing, and the regions are then merged with the help of fruitfully-based optimization. The authors report a Dice coefficient of 0.987 for EAT segmentation, slightly larger than [PERSON] et al. [20] on the same dataset. Another example of such an approach is presented in [PERSON] et al. [48]. The fuzzy c-means clustering to segment EAT from CT images. The method first segments a VoI of the heart on each CT slice using morphological operators and thresholding. Each slice then goes through a clustering step with a predefined number of clusters, where the features used to determine the clusters are the mean luminance and luminance variance of a patch of pixels. The clustering begins from a user-specified seed point placed on a single slice of the patient's CT scan. The resulting cluster of EAT is then post-processed by fitting an ellipse on the cluster and discarding the pixels outside the ellipse. The authors report Dice coefficients from 0.6 to 0.82 on 8 patients. 2 out of the 10 patients show unacceptable error rates of over 15% and were not included in the Dice coefficient measurements.  
From the reported results it would seem that machine learning-based direct EAT segmentation performs very well, however it is not clear how representative these results are of real-world performance. The testing data in [45] consists of a random sampling of 34% of the slices of 16 patients (4 patients were discarded to reduce training times). This means that their model was trained and evaluated on different slices of the same patient, allowing potential overfitting of a patient's anatomy. It is not clear if [20,46] use the same validation approach. Ref. [47] also does not clarify if the authors used a separate training and testing dataset to evaluate the classification performance, or how many slices and patients the whole process was evaluated on.