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A U-Shaped Network Based on Multi-level Feature and Dual-Attention Coordination Mechanism for Coronary Artery Segmentation of CCTA Images

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Abstract

Purpose

Computed tomography coronary angiography (CCTA) images provide optimal visualization of coronary arteries to aid in diagnosing coronary heart disease (CHD). With the deep convolutional neural network, this work aims to develop an intelligent and lightweight coronary artery segmentation algorithm that can be deployed in hospital systems to assist clinicians in quantitatively analyzing CHD.

Methods

With the multi-level feature fusion, we proposed Dual-Attention Coordination U-Net (DAC-UNet) that achieves automated coronary artery segmentation in 2D CCTA images. The coronary artery occupies a small region, and the foreground and background are extremely unbalanced. For this reason, the more original information can be retained by fusing related features between adjacent layers, which is conducive to recovering the small coronary artery area. The dual-attention coordination mechanism can select valid information and filter redundant information. Moreover, the complementation and coordination of double attention factors can enhance the integrity of features of coronary arteries, reduce the interference of non-coronary arteries, and prevent over-learning. With gradual learning, the balanced character of double attention factors promotes the generalization ability of the model to enhance coronary artery localization and contour detail segmentation.

Results

Compared with existing related segmentation methods, our method achieves a certain degree of improvement in 2D CCTA images for the segmentation accuracy of coronary arteries with a mean Dice index of 0.7920. Furthermore, the method can obtain relatively accurate results even in a small sample dataset and is easy to implement and deploy, which is promising. The code is available at: https://github.com/windfly666/Segmentation.

Conclusion

Our method can capture the coronary artery structure end-to-end, which can be used as a fundamental means for automatic detection of coronary artery stenosis, blood flow reserve fraction analysis, and assisting clinicians in diagnosing CHD.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 62273082 and No. 61773110), the Natural Science Foundation of Liaoning Province (No. 20170540312 and No. 2021-YGJC-14), the Basic Scientific Research Project (Key Project) of Liaoning Provincial Department of Education (LJKZ00042021), the Natural Science Foundation Joint Guidance Project of Heilongjiang Province (No. LH2020C001), and Fundamental Research Funds for the Central Universities (No. N2119008). This work was also supported by the Shenyang Science and Technology Plan Fund (No. 21-104-1-24, No. 20-201-4-10, and No. 201375), and the Member Program of Neusoft Research of Intelligent Healthcare Technology, Co. Ltd. (No. MCMP062002).

Author Contributions

PH: Conceptualization, Methodology, Software, Formal analysis, Writing-Original Draft and Editing. YD: Formal analysis, Validation, Writing-Review and Editing. DC: Writing-Review and Editing. CP: Writing-Review and Editing. BY: Formal analysis, Resources. LX: Formal analysis, Validation, Resources, Writing-Review and Editing, Funding acquisition. LG: Formal analysis, Validation.

Citation Diversity

Recent work in several fields of science has identified a bias in citation practices such that papers from women and other minority scholars are undercited relative to the number of papers in the field.\(^{2,3,10,11,14,18}\) We recognize this bias and have worked diligently to ensure that we are referencing appropriate papers with fair gender and racial author inclusion.

Conflict of interest

None declared.

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Author notes

  1. Peng Hong and Yong Du contributed equally to this work.

Authors and Affiliations

  1. Software College, Northeastern University, Shenyang, 110169, China

    Peng Hong & Dongming Chen

  2. Neusoft Research of Intelligent Healthcare Technology, Co. Ltd, Shenyang, 110169, China

    Peng Hong & Chengbao Peng

  3. College of Intelligence and Computing, Tianjin University, Tianjin, 300072, China

    Yong Du

  4. School of Electrical and Information Engineering, Northeast Agricultural University, Harbin, 150001, China

    Yong Du

  5. Department of Radiology, General Hospital of Northern Theater Command, Shenyang, 110169, China

    Benqiang Yang

  6. College of Medicine and Biological and Information Engineering, Northeastern University, Shenyang, 110167, China

    Benqiang Yang & Lisheng Xu

  7. Key Laboratory of Medical Image Computing, Ministry of Education, Shenyang, 110169, China

    Lisheng Xu

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  1. Peng Hong
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Correspondence to Dongming Chen or Chengbao Peng.

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Associate Editor Zhenglun Alan Wei oversaw the review of this article.

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Hong, P., Du, Y., Chen, D. et al. A U-Shaped Network Based on Multi-level Feature and Dual-Attention Coordination Mechanism for Coronary Artery Segmentation of CCTA Images. Cardiovasc Eng Tech 14, 380–392 (2023). https://doi.org/10.1007/s13239-023-00659-1

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