## 6 Magnetic Resonance Imaging and Artificial Intelligence
### AI Application in MRI Study for Vessel Segmentation  
The application of artificial intelligence in the characterization of atherosclerotic plaques consists of the creation of automated algorithms from quantitative information from magnetic resonance studies.  
Computer-aided manual tracing of the vessel boundaries obtained from MRI images was the first methodology created for this purpose. A method based on \"snakes\" called the active contours technique [147], which is able to disclose the irregularities of the vascular profile, proved to be accurate in carotid disease research in vivo [148] and in vitro [149]. However, the operator's dependence on the outline of the contour is a limitation for the accuracy of these methods. Automated post-processing algorithms and automated segmentation networks for studying the vessel wall have been proposed to overcome this problem.  
[PERSON] et al. [150] proposed a translationally equivariant CNN model based on 2D MRI images to obtain an automated outlining of the vascular contour. The method, based on the criteria used in the Carotid Artery Vessel Wall Segmentation Challenge [151], showed a good performance assessed by the estimation of the Dice similarity coefficient and Hausdorff distance, with the limit of the reduction of the accuracy in the case of the segmentation of horizontal segments in the vessel.  
In 2004, [PERSON] et al. [152] proposed a fully automated segmentation system to distinguish the different layers and components of atherosclerotic plaques. This algorithm was generated from plaque contour images obtained from T1-weighted and proton density-weighted in vivo MRI and provided to the segmentation of the arterial wall using fuzzy clustering. This approach is based on the analysis in different phases to identify the three different regions (lumen, outer wall, and lipid core), identifying vulnerable plaques based on the fibrotic cap thickness and the plaque area. The study results in a valid algorithm for estimating the different degrees of stenosis in carotid arteries, confirming previous data obtained from studies in vitro [153].  
Based on convolutional neural networks, [PERSON] et al. [154] proposed a deep morphology-aided diagnosis (DeepMAD) network for the early diagnosis of carotid atherosclerosis using the black-blood vessel wall-MRI study. One thousand and fifty-seven carotid MRI scans from the CAREII dataset [140] were collected and used to create automated software for carotid wall segmentation. DeepMAD comprises two parts, the segmentation subnetwork and the diagnosis subnetwork. The presence of two datasets improves the software performance, providing the automated diagnosis of atherosclerotic disease based on tracing the lumen and outer wall areas, with comparable results with the manual marking of the wall boundaries.  
Recently, a 3D meshed model of the carotid bifurcation and smaller branches was proposed [155]. The model results from implementing a U-net-based CNN algorithm through the combination of active morphological contours by iterative frameworks. Multi-spectral magnetic resonance imaging series images of the carotid circle of patients selected in the TAXINOMISIS study [156] were used for the 3D reconstruction of the carotid vascular tree. This approach overcomes the limit of manual segmentation, making available an accurate and automated model. Despite the initial promising results, the model must be fully validated in larger datasets.