Carotid Artery Segmentation Using Convolutional Neural Network in Ultrasound Images

Abstract

Cardiovascular disease (CVD) is one of the leading causes of death in urban areas. Carotid artery segmentation is the initial step in the automated diagnosis of carotid artery disease. The segmentation of carotid wall and lumen region boundaries are used as an essential part in assessing plaque morphology. In this paper, two types of Convolutional Neural Network (CNN) architectures are used for segmentation: U-Net and SegNet. The models used in this paper are applied on 257 ultrasound images containing a transverse section of the vessel acquired by ultrasound. Ultrasound imaging is noninvasive, completely unharming for the patient and a low-cost imaging method, but the main challenge when working with this kind of images is a very low signal to noise ratio and the process of imaging is highly dependent on the device operator. Different models are tested for various ranges of hyperparameter values and compared using different metrics. The model presented in this paper achieved over 94% Dice Coefficient for wall and lumen segmentation when trained during 100 epochs.