# Ultrasound Intima-Media Complex (IMC) Segmentation Using Deep Learning Models
## 1 Introduction  
The primary mechanism in the human body that sustains life is the cardiovascular system. Cardiovascular system diseases (CVDs) have been regarded as a major cause of death in the world. Lifespan can be increased and the death rate from CVDs can be decreased with early diagnosis and treatment of the diseases. The cardiovascular system is made up of blood vessels that carry blood, necessary for all of the body's organs to operate. The primary components of the blood vessels that transport blood to and from the heart and to all organs are arteries and veins. Any obstruction in blood flow or disease in the arteries or veins will seriously affect how well the organs operate. The most common types of cardiovascular disease include peripheral vascular disease, coronary artery disease and carotid artery disease. These disorders manifest as a result of the development of atherosclerotic plaques in the arteries, as illustrated in Figure 1. One of the effects of carotid artery stenosis is an ischemic stroke, due to the accumulation of plaque on the carotid arterial walls. If the stenosis is detected early and the amount of plaque can be determined, the problem can be addressed immediately. For this, a variety of imaging modalities are used. Computed tomography (CT), EEG, ECG, ultrasound imaging, laboratory tests for coagulation status and cardiac monitoring are among the diagnostic techniques used in the assessment of carotid artery stenosis or stroke. Both sides of the neck contain the common carotid artery. The soft tissue features in the arteries allow for imaging using a variety of methods or modalities, such as computerized tomography (CT), ultrasound imaging and magnetic resonance imaging. The analysis of the generated images can enhance diagnosis and support clinical judgment. Medical image analysis algorithms haveadvanced significantly from image processing and pattern recognition methods to machine learning and deep learning algorithms that see it as a computer vision problem. A notable development in the automatic segmentation, analysis and grading of stenosis is the use of carotid artery imaging generated by CT scans, MRIs and ultrasound images [1; 2]. Due to the complexity of scanning the carotid artery, ultrasound scanning is the preferred method to capture images with acceptable resolutions. Ultrasound images have been used for many studies using medical imaging analysis algorithms [3]. In order to segment the plaques on the carotid artery, many methods have been proposed even in the absence of large datasets. Previously, the proposed methods used CIMT measurement to detect and localize the carotid artery walls and then the plaques [4; 5]. The ground truth was presented using some points representing the plaques generated by specialists [6]. The analysis using these types of data used different statistical and machine learning algorithms, including Snake's segmentation and contour [4; 5], bulb edge detection [6], wind-driven optimization techniques [7] and SVM [8]. Using convolution neural networks, the proposed methods used binary segmentation instead of CIMT measurement. By generating binary images containing labeled regions in the images instead of using points, the deep learning methods could successfully segment these regions with better precision [8]. Furthermore, the segmented regions could be helpful in computing CIMT [9], related to the performance accuracy of segmentation. This makes segmentation a crucial task. Although CNNs have succeeded in solving many computer vision problems, recent studies have shown many drawbacks for CNNs, such as the need for large datasets [10] and the reliance on linear neuron models [11; 12; 13; 14]. Operational neural networks (ONNs) [14; 15; 16; 17] are heterogeneous networks with a non-linear neuron model that have recently been proposed as a solution for highly non-linearly separable problems. With the help of predefined nodal, pool and activation operators, ONNs are able to learn highly complex and multi-modal functions.