## 3 Image Processing Approaches Used for EAT and PAT Segmentation
### Deep Learning  
Deep learning extends the concepts of machine learning. In machine learning, there is a known statistical model that learns its parameters from the training data. In deep learning, the models themselves are learned from the training data. To achieve this, deep learning methods use artificial neural networks (ANNs) to perform tasks [34]. ANNs are a type of computing system that consists of multiple connected artificial neurons. Each neuron is a mathematical function that transforms one or more input matrices into an output matrix by summing all the input matrices, multiplied by their respective weights, and then feeding the sum into an activation function, whose job is usually to add an element of non-linearity. Deep learning ANNs have a layered structure, starting from the input layer, which receives the input data. Results from the input layer's neurons are then passed through a series of one or more hidden layers. Finally, the results are passed to an output layer, which offers the prediction for the task at hand. The quality and shape of the output are determined by the network architecture, i.e., the number of neurons, how they are connected, and which activation functions are used.  
The most common types of ANNs used for medical image processing are convolutional neural networks (CNNs). CNNs are ANNs that use the mathematical operation of convolution instead of summation in one or more of their neurons. Usually, they contain several layers of this type of neuron called convolutional layers. CNNs are particularly good at working with image data. In CNNs, the first layers of the network generally learn to recognize rudimentary features like edges and shapes. In later layers, the earlier features are combined to form more complex features, while in the deepest layers, the network can often detect very specific shapes like the wheels of a car or eyes on a face.  
A common CNN architecture used in medical imaging is U-Net [35], shown to be a state-of-the-art method in a variety of tasks. Variations of U-Net are commonly used for the task of EAT segmentation [36; 37; 38]. The U-Net architecture consists of a cascade of layers that progressively downsample the image, called the contracting path, followed by another cascade of layers called the expanding path that upsamples the image back into the output image that contains the segmented portions. Outputs of layers of the contracting path are connected directly to the matching layers of the expanding path. This allows the network to concurrently use the global context from the contracting path with complex local features of the expanding path to achieve good segmentation accuracy. The U-Net architecture is shown in Figure 8.  
In terms of segmentation time, neural networks are usually an order of magnitude faster than more traditional image processing methods and can sometimes achieve much better accuracy [39]. However, deep neural networks require a large amount of training data, which can be challenging, especially in medical imaging tasks. Additionally, neural networks are hard to interpret. Since their models' parameters are learned from the data and not programmed by hand, the way they work and their specific limitations are not known ahead of time and are hard to test without access to a large amount of testing data.