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Automated Segmentation Based on Deep Learning of the MR Vessel Wall Imaging

The rupture of unstable or vulnerable atherosclerotic plaque is the major cause of ischemic stroke. Manual analysis of the vessel wall and plaque is labor-intensive and experience-dependent. The purpose of this study is to develop an automatic method to segment the vessel wall and lumen contour for quantitative measurement. In this work, a CNN architecture for fully automated segmentation of arterial lumen and vessel wall on MR vessel wall images was developed and evaluated on ischemic stroke patients. In conclusion, we proposed a good performance automatic analysis method for the vessel wall and is important for plaque analysis.