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Abstract #4738

Towards Domain-invariant Carotid Artery Lumen-wall Segmentation Using Adversarial Networks

Anna Danko1,2, Roberto Souza2,3, and Richard Frayne2,3

1Medical Sciences Graduate Program, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada, 2Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, AB, Canada, 3Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada

Magnetic resonance (MR) imaging is frequently used for carotid artery wall imaging. The capacity for multi-contrast imaging allows MR scanners to resolve the lumen and wall, as well as multiple plaque components. Combined this information can provide evidence of increased stroke risk. Quantitative analysis of carotid artery MR images regularly begins with the manual segmentation of wall and plaque. This process is time-consuming and costly, and suggests the need for automated methods. Developing a robust segmentation tool is challenging because of the domain shift due to different image contrasts and/or scanners. Here, we demonstrate that a deep learning network including an adversarial component is capable of learning domain-invariant features, thus producing a generalizable segmentation model.

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