Estimation of the Arterial Diameter in Ultrasound Images of the Common Carotid Artery

This paper addresses a fully automatic segmentation method for ultrasound images of the common carotid artery. The goal of this procedure is the detection of the arterial walls to assist in the evaluation of the arterial diameter. In other words, the main objective is the segmentation of the region corresponding to the lumen of the vessel, where the blood flows. The evaluation of the Lumen Diameter (LD) provides useful information for the diagnosis of arterial diseases. The monitoring of LD and Intima-Media Thickness (IMT) is crucial in the early detection of atherosclerosis and in the assessment of the cardiovascular risk. The proposed methodology is completely based on Machine Learning and it applies Auto-Encoders and Deep Learning to obtain abstract and efficient data representations. Thus, the segmentation task is posed as a pattern recognition problem. The different architectures designed have shown a good classification performance. In addition, the results obtained for some ultrasound images of the common carotid artery can be visually validated in this work. The final automatic segmentation is quite accurate, and it is possible to conclude that it will lead to a precise and reliable measurement of the lumen diameter.