Patch-based Convolutional Neural Network for Atherosclerotic Carotid Plaque Semantic Segmentation

Abstract

Atherosclerotic plaque deposition within the coronary vessel wall leads to arterial stenosis and if not adequately treated, it may potentially have deteriorating consequences, such as a debilitating stroke, thus making early detection of the most importance. The manual plaque components annotation process is both time and resource consuming, therefore, an automatic and accurate segmentation tool is necessary. The main aim of this paper is to present the model for identification and segmentation of the atherosclerotic plaque components such as lipid core, fibrous and calcified tissue, by using Convolutional Neural Network on patch-based segments of ultrasound images. There was some research done on the topic of plaque components segmentation, but not in ultrasound imaging data. Due to the size of some plaque components being only a couple of millimeters, we argue that training a neural network on smaller image patches will perform better than a classifier based on the whole image. Besides the size of components, this decision is motivated by the observation that plaque components are not uniformly distributed throughout the whole carotid wall and that a locality-sensitive segmentation is likely to obtain better segmentation accuracy. Our model achieved good results in the segmentation of fibrous tissue but had difficulties in the segmentation of lipid and calcified tissue due to the quality of ultrasound images.