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SC-Unext: A Lightweight Image Segmentation Model with Cellular Mechanism for Breast Ultrasound Tumor Diagnosis

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Abstract

Automatic breast ultrasound image segmentation plays an important role in medical image processing. However, current methods for breast ultrasound segmentation suffer from high computational complexity and large model parameters, particularly when dealing with complex images. In this paper, we take the Unext network as a basis and utilize its encoder-decoder features. And taking inspiration from the mechanisms of cellular apoptosis and division, we design apoptosis and division algorithms to improve model performance. We propose a novel segmentation model which integrates the division and apoptosis algorithms and introduces spatial and channel convolution blocks into the model. Our proposed model not only improves the segmentation performance of breast ultrasound tumors, but also reduces the model parameters and computational resource consumption time. The model was evaluated on the breast ultrasound image dataset and our collected dataset. The experiments show that the SC-Unext model achieved Dice scores of 75.29% and accuracy of 97.09% on the BUSI dataset, and on the collected dataset, it reached Dice scores of 90.62% and accuracy of 98.37%. Meanwhile, we conducted a comparison of the model’s inference speed on CPUs to verify its efficiency in resource-constrained environments. The results indicated that the SC-Unext model achieved an inference speed of 92.72 ms per instance on devices equipped only with CPUs. The model’s number of parameters and computational resource consumption are 1.46M and 2.13 GFlops, respectively, which are lower compared to other network models. Due to its lightweight nature, the model holds significant value for various practical applications in the medical field.

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Data Availability

Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.

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Acknowledgements

Thanks to the following medical centers for providing data support: the First Affiliated Hospital of Chongqing Medical University, the Second Affiliated Hospital of Chongqing Medical University, University-Town Hospital of Chongqing Medical University.

Funding

This work is supported by the Chongqing Municipal undergraduate universities and institutes affiliated to the Chinese Academy of Sciences in 2021 under Grant No.HZ2021015, Key Project of Chongqing Education Commission’s Science and Technology Research Program under Grant No.KJZD-K202301505, Chongqing Medical Scientific Research Project (joint project of Chongqing Health Commission and Science and Technology Bureau, No.2022MSXM041), the Future Medical Youth Innovation Team Development Support Plan of Chongqing Medical University (Scientific Research and Innovation Team, No. W0169), Chongqing Natural Science Foundation General Project (No.CSTB2022NSCQ-MSX0152).

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Author notes

  1. Fenglin Cai and Jiaying Wen contributed equally to this work.

Authors and Affiliations

  1. Department of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, 401331, People’s Republic of China

    Fenglin Cai, Fangzhou He & Jie Li

  2. Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People’s Republic of China

    Jiaying Wen, Yulong Xia, Weijun Xu, Yong Zhang & Li Jiang

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  1. Fenglin Cai
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  2. Jiaying Wen
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  3. Fangzhou He
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  7. Li Jiang
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  8. Jie Li
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Contributions

All authors contributed to the study conception and design. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Li Jiang or Jie Li.

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This study is designed based on the guidelines for human studies approved by the Ethics Committee of the First Affiliated Hospital of Chongqing Medical University and was ethically in accordance with the Helsinki Declaration.

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Informed consent is obtained from all study participants for this study. This research obtains written consent for publication from the parents or legal guardians of all participants in the research.

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Cai, F., Wen, J., He, F. et al. SC-Unext: A Lightweight Image Segmentation Model with Cellular Mechanism for Breast Ultrasound Tumor Diagnosis. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-024-01042-9

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  • DOI: https://doi.org/10.1007/s10278-024-01042-9

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