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Spatial multi-scale attention U-improved network for blood vessel segmentation

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Abstract

Vessel segmentation in digital subtraction angiography (DSA) is of great significance for the diagnosis, evaluation and detection of cerebral diseases. Manual segmentation is relatively time-consuming and subjective, so that automatic cerebrovascular segmentation technology has good application value in the treatment of cerebrovascular diseases. The traditional segmentation algorithm performs poorly because of the complexity of cerebrovascular structure, large-scale changes, and the impact of noise such as artifacts in DSA. In this work, using depth learning method and attention mechanism, we propose a spatial multi-scale attention U improved network (SMAU-Net) for vessel segmentation of DSA images. The network mainly consists of three parts: multi-scale spatial attention module, feature aggregate module, and detail supervision module. Using various attention mechanisms to pay attention to scale, space and channel information, the semantic, edge information and thin vessel features are enhanced. We applied the proposed method to the benchmark retinal vessel dataset CHASE and DSAC (cerebral DSA imaging dataset made in our laboratory). The experimental results show that the proposed SMAU-Net achieves the F1 score of 86.32% and the precision of 90.04%, which is superior to other models and is an improvement of 1.98 and 5.71% over the baseline U-Net. The experiment also proves that the method can be extended to various vascular segmentation tasks and has good visual diagnosis quality.

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 81901190, in part by the Nature Science Foundation of Heilongjiang Province under Grant LH2020F021, in part by the Fundamental Research Funds for the Central Universities under Grant 3072021CFT0802.

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Author notes

  1. Shan Gao contributed equally to this work.

Authors and Affiliations

  1. College of Information and Communication Engineering, Harbin Engineering University, Nantong Street, Harbin, 150001, Heilongjiang, China

    Ying Cui, Jingjing Su, Jia Zhu, Liwei Chen & Shan Gao

  2. Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Nantong Street, Harbin, 150001, Heilongjiang, China

    Ying Cui, Jingjing Su, Jia Zhu, Liwei Chen & Shan Gao

  3. Department of Neurosurgery, First Affiliated Hospital of Harbin Medical University, Youzheng Street, Harbin, 150001, Heilongjiang, China

    Guang Zhang

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  1. Ying Cui
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  2. Jingjing Su
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  3. Jia Zhu
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  4. Liwei Chen
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  5. Guang Zhang
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Contributions

Ying Cui and Jingjing Su designed the framework, implemented the method and wrote the manuscript. Jia Zhu and Liwei Chen made datasets. Guang Zhang provides support in DSA image acquisition. Shan Gao helped to revise the manuscript language. All authors reviewed the manuscript.

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Correspondence to Shan Gao.

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Cui, Y., Su, J., Zhu, J. et al. Spatial multi-scale attention U-improved network for blood vessel segmentation. SIViP 17, 2857–2865 (2023). https://doi.org/10.1007/s11760-023-02504-3

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  • DOI: https://doi.org/10.1007/s11760-023-02504-3

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