基于深度卷积和多层尺度特征融合的冠脉造影图像血管分割

doi:10.3969/j.issn.0258-8021.2025.01.004

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Abstract Coronary angiography is a significant diagnostic and therapeutic modality for coronary heart disease and other cardiovascular diseases. The accurate and expeditious segmentation of blood vessels is of paramount importance to the diagnosis and treatment of cardiovascular diseases. Existing coronary angiography vessel segmentation algorithms have been shown to have several shortcomings, including a weak segmentation ability for fine vessels, poor connectivity of segmented vessels, and a lack of resistance to noise and artefacts. This study proposes an enhanced U-shape segmentation network, termed HAM-UNet.UNet, which utilises the advantages of the Transformer structure′s long-distance dependence and cross-domain hopping connectivity. The proposed methods include contextual hierarchical aggregation and multiscale feature fusion. Firstly, a series of image preprocessing methods are employed to enhance certain features of the original coronary angiography images and expand the experimental data. Then, the preprocessed images are segmented by the HAM-UNet method.The encoder combines both deep convolution and residual structure, which can efficiently capture global features and effectively enhance the network detail perception, thus improving the segmentation accuracy while increasing the segmentation connectivity. The decoder performs multi-scale feature fusion and up-sampling hopping connections, improving the global perception of the network and reducing the influence of irrelevant information.The datasets used are from 221 images from the General Hospital of Tianjin Medical University and 494 images from the First Hospital of Qinhuangdao City. On both datasets, the HAM-UNet algorithm achieves an accuracy of 0.983 and 0.998, respectively. As demonstrated in Figure 8, the IOUs are 0.857 and 0.908, and the Dice scores are 0.842 and 0.883, respectively. This indicates that the overall segmentation performance is superior to that of U-Net, Att-UNet and other algorithms.

Key words： image segmentation coronary angiography image segmentation U-Net deep convolution multi-scale fusion

Received: 17 May 2024

PACS:

R318

Corresponding Authors: *E-mail:wjj@ysu.edu.cn

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	Xu Yang
	Zhai Nannan
	Ni Weizhen
	Tan Qiang
	Wang Jinjia

Cite this article:

Xu Yang,Zhai Nannan,Ni Weizhen, et al. Vessel Segmentation in Coronary Angiography Images Based on Deep Convolution and Multi-Level Scale Feature Fusion[J]. Chinese Journal of Biomedical Engineering, 2025, 44(1): 34-42.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2025.01.004 OR http://cjbme.csbme.org/EN/Y2025/V44/I1/34

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