Deep Learning-Based Automatic Segmentation of Arterial Plaques in MR Vessel Wall Images

Manual segmentation of atherosclerotic plaque for quantitative assessment is a time-consuming process. In this study, a convolutional neural network based automatic segmentation method named Vessel-Segnet was proposed for quantitative evaluation of lumen, vessel wall and plaque based on MR vessel wall images. The proposed method achieved the best segmentation performance with the highest dice similarity coefficient and the lowest average surface distance among six models. In terms of morphological quantitative evaluation, the proposed method achieved excellent agreement with manual method. Overall, the proposed method can quickly and accurately realize the segmentation of lumen, vessel wall and plaque for quantitative evaluation.