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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