Carotid artery atherosclerosis is one of the main causes of stroke and there is a pressing need for a non-invasive method to quantify, monitor and assess carotid artery stenosis, composition and distensiblity. Here we focus on developing a fully automated convolutional neural network (CNN) with time-context for segmenting the common carotid artery lumen from dynamic magnetic resonance images. The challenge in developing a fully automated carotid segmentation algorithm is that there are other vessels with size and spatial location comparable to the carotid artery. Our preliminary results indicate that a CNN with time-context is capable of distinguishing and segmenting the carotid artery from other vessels.
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