Background: Coronary heart disease is one of the diseases with the highest mortality rate. Due to the important\nposition of cardiovascular disease prevention and diagnosis in the medical field, the segmentation of cardiovascular\nimages has gradually become a research hotspot. How to segment accurate blood vessels from coronary\nangiography videos to assist doctors in making accurate analysis has become the goal of our research.\nMethod: Based on the U-net architecture, we use a context-based convolutional network for capturing more\ninformation of the vessel in the video. The proposed method includes three modules: the sequence encoder module,\nthe sequence decoder module, and the sequence filter module. The high-level information of the feature is extracted\nin the encoder module. Multi-kernel pooling layers suitable for the extraction of blood vessels are added before the\ndecoder module. In the filter block, we add a simple temporal filter to reducing inter-frame flickers.\nResults: The performance comparison with other method shows that our work can achieve 0.8739 in Sen, 0.9895 in\nAcc. From the performance of the results, the accuracy of our method is significantly improved. The performance\nbenefit from the algorithm architecture and our enlarged dataset.\nConclusion: Compared with previous methods that only focus on single image analysis, our method can obtain\nmore coronary information through image sequences. In future work, we will extend the network to 3D networks
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