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ACX-UNet: a multi-scale lung parenchyma segmentation study with improved fusion of skip connection and circular cross-features extraction

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Abstract

Convolutional neural networks (CNN) are widely used in the field of computer-aided diagnosis of lung diseases. Its main tasks are segmentation of lung parenchyma, lung nodule detection and lesion analysis. Among them, the accurate segmentation of lung parenchyma is the key step to achieve lung nodule detection and lung disease diagnosis. Inspired by the self-attention mechanism in transformer, many excellent variants of the attention module combined with convolutional neural networks have been applied to lung image segmentation. However, existing models usually suffer from feature information loss and insensitive multi-scale feature extraction. In this paper, we propose a convolutional neural network ACX-UNet incorporating attention mechanism and cyclic cross-feature extraction in an encoding–decoding mode for lung image segmentation. The convolutional block attention mechanism module (CBAM) is introduced into the skip connection part of the network to reduce the loss of feature information by increasing the attention of high-resolution features; the proposed CX-SPP module is used in the decoding part instead of the two-layer convolution of UNet, and the key information output is cyclically superimposed, deepening the network level and fusing multi-scale information to highlight the target region to suppress the background pixel interference; in order to obtain richer semantic information and improve the generalization ability of the model, the pre-trained VGG16 network is migrated to the encoder part of the UNet model to achieve feature migration and parameter sharing and reduce the training cost. Comparative experiments and ablation experiments are conducted on LIDC and LUNA public datasets with six existing mainstream segmentation networks, and the results show that the prediction maps obtained by the model in this paper are closer to the real labels, and the mIoU, mPA, Precision and Recall evaluation metrics are better than other comparison models.

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Data availability

The data that support the findings of this study are available on request from the corresponding author.

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Funding

National Natural Science Foundation of China (61972456,61173032); Tianjin Research Innovation Project for Postgraduate Students (2022SKY126); National Student Innovation Training Program(202310058029).

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Author notes

  1. Hongbing Wu, Zhuo Zhang and Yuchen Zhang have equally contributed to this work.

Authors and Affiliations

  1. Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, School of Computer Science and Technology, Tiangong University, Tianjin, 300387, China

    Hongbing Wu & Baoshan Sun

  2. School of Electronic and Information Engineering, Tiangong University, Tianjin, 300387, China

    Zhuo Zhang

  3. School of Economics and Management, Tiangong University, Tianjin, 300387, China

    Yuchen Zhang

  4. Tianjin Cerebral Vascular and Neural Degenerative Disease Key Laboratory, Tianjin Huanhu Hospital, Tianjin, 300350, China

    Xiaochen Zhang

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  1. Hongbing Wu
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  2. Zhuo Zhang
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  3. Yuchen Zhang
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  4. Baoshan Sun
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Contributions

HW, ZZ, and YZ wrote the main manuscript text; BS and XZ made critical revisions to the paper and provided fund support. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Baoshan Sun or Xiaochen Zhang.

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Wu, H., Zhang, Z., Zhang, Y. et al. ACX-UNet: a multi-scale lung parenchyma segmentation study with improved fusion of skip connection and circular cross-features extraction. SIViP 18, 525–533 (2024). https://doi.org/10.1007/s11760-023-02770-1

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  • DOI: https://doi.org/10.1007/s11760-023-02770-1

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