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Abstract #3511

CNN-based segmentation of vessel lumen and wall in carotid arteries from T1-weighted MRI

Lilli Kaufhold1,2, Axel J. Krafft3, Christoph Strecker4, Markus Huellebrand1,2, Ute Ludwig3, Jürgen Hennig3, Andreas Harloff4, and Anja Hennemuth1,2
1Charité - Universitätsmedizin Berlin, Berlin, Germany, 2Fraunhofer MEVIS, Bremen, Germany, 3Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine, Freiburg, Germany, 4Department of Neurology, Medical Center – University of Freiburg, Faculty of Medicine, Freiburg, Germany

Internal carotid artery stenosis is a major source of ischemic stroke. Multi-contrast MRI can be used for assessing wall characteristics and plaque progression. The quantification of vessel wall morphology requires an accurate segmentation of the vessel wall. To reduce inter- and intra-observer variability, we aim to provide a fully automatic segmentation method. Our approach for segmenting the lumen and vessel wall of the extracranial carotid arteries in T1-weighted 3D MR images is based on a 2D convolutional neural network. Average dice coefficients were 0.947/0.859 for the lumen/vessel wall and the median Hausdorff-distance was below the voxel-size of 0.6mm for both.

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