# Ultrasound Intima-Media Complex (IMC) Segmentation Using Deep Learning Models
## 2 Related Works  
The carotid artery segmentation, including the walls and plaques in the intima-media complex (IMC), can be used for the estimation of intima-media thickness (IMT). Which makes it an important operation for atherosclerotic risk evaluation.  
There are numerous methods for segmenting the intima-media complex. However, the majority of them are semi-automatic and require manual intervention. Medical experts must define the boundary between the media adventitia and lumen. However, the subjectivity and variability of manual segmentation can be reduced using image segmentation algorithms. Additionally, IMT is assessed using active contours [21; 22; 23; 24; 25; 26; 27; 28], dynamic programming [29; 30; 31; 32; 33; 34] and edge detection algorithms and gradient-based approaches [35; 36]. For active contour-based approaches, the authors in [21] began with a simple segmentation of B-mode ultrasound images followed by segmentation of the far wall intima-media-adventitia, then applied the active contour to obtain the desired region in the images. The same process was used in [22], but this time using some morphological operations, such as opening. Subsequently, an LI contour function was applied to detect the final common carotid artery result. In [23], the authors started with non-linear filtering followed by the detection of the intima layer using an iterative relaxation procedure to detect the wall using a modified energy function and an optimal initial contour.  
For dynamic programming-based approaches, the researchers in [29] used a multi-scale dynamic programming (DP) algorithm to estimate the vessel wall positions leading to boundary detection. The obtained results with geometrical characteristics were used to obtain the final results. In the same context and to detect the arterial wall, the authors in [31] proposed a dual dynamic programming (DDP) technique to detect the intima and adventitial layers of the common carotid artery. Furthermore, in [33] an improved dynamic programming method was proposed for carotid artery wall thickness evaluation.  
Machine and deep learning techniques have becoming intriguing as promising methods for medical image analysis tasks, such as image de-noising, segmentation and classification. Before the development of deep learning models, machine learning was the most commonly utilized technology, where comprehensive feature extraction techniques were applied to find several areas of carotid artery risk estimation. The deep learning strategy takes advantage of a neural network architecture that mimics the human brain by having more hidden layers. The neuron is the fundamental building block of a deep neural network (DNN), which accepts several inputs, linearly combines them and then passes them to a non-linear network to produce the desired output. Multiple processing layers make up a deep learning network, which uses deep graphs to extract high-level representations of meaningful information from low-level inputs. CNNs are among the most widely used networks in the medical image analysis domain [37]. U-Net is a CNN-based architecture used to solve the automatic image segmentation problem. This architecture has been adopted in many IMC segmentation works [38; 39; 40]. For example, in [38; 41] the authors used the U-Net architecture for plaque segmentation in carotid ultrasound images.  
Furthermore, in [42] the authors used U-Net, U-Net+, U-Net++, U-Net++, U-Net++ and three types of hybrids, namely, Inception-U-Net, Fractal-U-Net and Squeeze-U-Net architectures, to segment and measure the plaque far wall area of the common carotid (CCAs) and internal carotid arteries (ICAs) in B-mode ultrasound images. Using M-Net [43] as the backbone, the authors in [44] proposed an automatic joint segmentation method named CSM-Net with triple spatial attention and cascaded dilated convolution modules.