Automatic carotid ultrasound segmentation using deep convolutional neural networks and phase congruency maps

Abstract

The segmentation of media-adventitia and lumen-intima boundaries of the Carotid Artery forms an essential part in assessing plaque morphology in Ultrasound Imaging. Manual methods are tedious and prone to variability and thus, developing automated segmentation algorithms is preferable. In this paper, we propose to use deep convolutional networks for automated segmentation of the media-adventitia boundary in transverse and longitudinal sections of carotid ultrasound images. Deep networks have recently been employed with good success on image segmentation tasks, and we thus propose their application on ultrasound data, using an encoder-decoder convolutional structure which allows the network to be trained end-to-end for pixel-wise classification. Concurrently, we evaluate the performance for various configurations, depths and filter sizes within the network. In addition, we further propose a novel fusion of envelope and phase congruency data as an input to the network, as the latter provides an intensity-invariant data source to the network. We show that this data fusion and the proposed network structure yields higher segmentation performance than the state-of-the-art techniques.