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Abstract #3466

Automatic segmentation of the aortic arterial wall in a pre-clinical rabbit model of atherosclerosis: preliminary experience with a convolutional neural network

Daniel Samber1, Claudia Calcagno1, Edmund Wong1, Venkatesh Mani1, Cheuk Tang1, and Zahi A. Fayad1

1Icahn School of Medicine at Mount Sinai, New York, NY, United States

The task of manually evaluating medical images can be onerous, plagued by subjective bias, and subject to human error. In this study we apply a convolutional neural network (CNN) for automated image segmentation of the atherosclerotic vessel wall, a notoriously challenging and time consuming segmentation task. Our CNN shows a classification accuracy of 90% on testing data, and a intersection over union (IoU) weighted by the number of pixels in each class of 86%, indicating excellent segmentation. Our results suggest that, if appropriately optimized this method has the potential deliver faithful and automatic segmentation of the arterial vessel wall.

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