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Abstract #0439

Towards a fully Automated Time-context Sensitive Convolutional Neural Network for Common Carotid Artery Lumen Segmentation on Dynamic MRI

Roberto Souza1, Mariana Bento1, Lívia Rodrigues2, Letícia Rittner2, Roberto Lotufo2, and Richard Frayne1

1Seaman Family Magnetic Resonance Research Centre, Calgary, AB, Canada, 2Medical Image Computing Lab, Campinas, Brazil

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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