ArticleBMC medical imaging2020
Coronary angiography video segmentation method for assisting cardiovascular disease interventional treatment.
Article in BMC medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- MSA U-Net: a multi-scale attention-enhanced network for automatic segmentation of uterine lymph nodes in MRI images.Journal of the Egyptian National Cancer Institute · 2026Article
- Deep Learning-Based Segmentation of Coronary Arteries and Stenosis Detection in X-Ray Coronary Angiography.JACC. Advances · 2025Article
- A novel framework for segmentation of small targets in medical images.Scientific reports · 2025Article
- Prediction of fractional flow reserve with enhanced ant lion optimized support vector machine.Heliyon · 2023Article
- Coronary X-ray angiography segmentation using Artificial Intelligence: a multicentric validation study of a deep learning model.The international journal of cardiovascular imaging · 2023Article
- O-Net: A Novel Framework With Deep Fusion of CNN and Transformer for Simultaneous Segmentation and Classification.Frontiers in neuroscience · 2022Article
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Authors and funding
8 authors.
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Abstract
backgroundCoronary heart disease is one of the diseases with the highest mortality rate. Due to the important position of cardiovascular disease prevention and diagnosis in the medical field, the segmentation of cardiovascular images has gradually become a research hotspot. How to segment accurate blood vessels from coronary angiography videos to assist doctors in making accurate analysis has become the goal of our research.
methodBased on the U-net architecture, we use a context-based convolutional network for capturing more information of the vessel in the video. The proposed method includes three modules: the sequence encoder module, the sequence decoder module, and the sequence filter module. The high-level information of the feature is extracted in the encoder module. Multi-kernel pooling layers suitable for the extraction of blood vessels are added before the decoder module. In the filter block, we add a simple temporal filter to reducing inter-frame flickers.
resultsThe performance comparison with other method shows that our work can achieve 0.8739 in Sen, 0.9895 in Acc. From the performance of the results, the accuracy of our method is significantly improved. The performance benefit from the algorithm architecture and our enlarged dataset.
conclusionCompared with previous methods that only focus on single image analysis, our method can obtain more coronary information through image sequences. In future work, we will extend the network to 3D networks.
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