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Abstract #2098

Multi-class segmentation of the Carotid Arteries using Deep Learning

Magnus Ziegler1,2, Jesper Alfraeus1,2, Elin Good1,2,3, Jan Engvall1,2,3, Ebo de Muinck1,2,3, and Petter Dyverfeldt1,2

1Linköping University, Linköping, Sweden, 2Center for Medical Image Science and Visualization (CMIV), Linköping, Sweden, 3Linköping University Hospital, Linköping, Sweden

The rupture of atherosclerotic plaques in the carotid arteries can lead to strokes, which is one of the most common causes of death worldwide. MRI can provide geometric, compositional, and hemodynamic information about the carotid arteries, but in order to access this information, the images must first be segmented to delineate the regions of interest. This work proposes a state-of-the-art convolutional neural network, developed from the DeepMedic architecture, that performs automated, multi-class segmentation of the carotid arteries. Results show high quantitative and qualitative scores, with DICE = 0.8750, Sensitivity = 0.9374, Specificity = 0.9942, and F2 = 0.9067.

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