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DPSMUNet: a new network based on a dual-pooling self-attention module for carotid artery plaque segmentation in ultrasound images

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Abstract

Plaque segmentation in ultrasound images is essential for diagnosing carotid artery diseases but is hindered by challenges like noise, low contrast, and irregular plaque shapes. This paper proposes DPSMUNet, an enhanced UNet-based deep neural network incorporating a Dual Pooling Self-Attention Module (DPSM) and a Depthwise Separable Feedforward Network (DSFN). DPSM enables multi-scale global self-attention, while DSFN fuses semantic information across layers. Our experimental results, conducted on our carotid ultrasound image dataset as well as publicly available datasets (breast ultrasound image dataset and nerve ultrasound image dataset), demonstrate that DPSMUNet achieves strong performance across all metrics (DSC, IoU, recall, precision, parameter count, and inference time), with DSC, IoU, and recall being the most optimized. These findings suggest that DPSMUNet effectively segments plaque boundaries in carotid ultrasound images with relatively low computational complexity.

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Data Availability

No datasets were generated or analysed during the current study.

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Acknowledgements

Thanks to Dr. Meizhen Fu from Shanghai East Hospital for her contribution to the creation of the ground-truth segmentation maps. This study was funded by Medical discipline Construction Project of Pudong Health Committee of Shang-hai (Grant No. PWYgy2021-05).

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  1. Xiaolong Wang and Hedi An have contributed equally to this work.

Authors and Affiliations

  1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China

    Xiaolong Wang, Hedi An, Jinsong Zhang, Dongya Huang & Junxian Wen

  2. Department of Neurology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, 200120, China

    Xiaolong Wang, Hedi An, Jinsong Zhang & Dongya Huang

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Contributions

Wang Xiaolong and An Hedi wrote the main manuscript text and Zhang Jingsong reviewed and edited the writing. Huang Dongya supervised the draft preparation. Wen Junxian processed and checked the experimental data. All authors reviewed the manuscript.

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Correspondence to Jinsong Zhang or Dongya Huang.

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Wang, X., An, H., Zhang, J. et al. DPSMUNet: a new network based on a dual-pooling self-attention module for carotid artery plaque segmentation in ultrasound images. J Supercomput 81, 267 (2025). https://doi.org/10.1007/s11227-024-06770-x

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