Automated Diagnosis of Cardiovascular Disease Through Measurement of Intima Media Thickness Using Deep Neural...

Ultrasound images(US) of carotid artery aid in the detection and diagnosis of Cardiovascular Diseases (CVD). Traditional methods for analysis of US images employ hand crafted features to classify images, which need expert knowledge for careful design and lack robustness to variations, leading to low sensitivity in clinical applications. Intima Media Thickness (IMT) and elasticity are the predominant markers used for carotid artery (CA) atherosclerotic plaque detection. This paper proposes to address the problem by building Convolutional Neural Network (CNN) for segmentation of intima media complex (ie) Region of Interest (RoI). A dataset consisting of 450 subjects is used to train and validate the proposed CNN. Segmentation is done in the far wall region of the artery from the longitudinal B-mode images enabling atleast 24 RoIs and RoNIs (Region of Non Interest) for each image. The result of 10-fold cross validation shows accuracy of 99.54%. Mean deviation of IMT from manual tracings is found to be 0.06645mm.