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Lightweight multi-scale attention group fusion structure for nuclei segmentation

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Abstract

The intricacy of segmentation is intensified by the morphological variability of cell nuclei. While, the U-Net model can achieve commendable outcomes in such contexts, it encounters difficulties, including semantic inconsistencies between the encoder and decoder, as well as an excessive number of parameters. To tackle these challenges, this study presents a lightweight multi-scale attention fusion (MAF) module intended to supplant conventional skip connections, with the goal of alleviating semantic discrepancies arising from multi-level downsampling and upsampling processes. Specifically, we establish multi-scale skip connections utilizing various attention mechanisms to enhance the flow of information. Furthermore, we introduce a deformable attention Condense (DACond) module designed to replace convolution operations, thereby reducing the overall parameter count and enhancing ability to learn the shape of the nucleus. The proposed model is designated as Lightweight Multi-scale Attention Fusion UNet (LMAF-UNet). Our LMAF-UNet exhibits enhanced segmentation performance across four publicly available datasets, while concurrently minimizing parameter size and computational complexity.

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Funding

This work is supported by the Natural Science Starting Project of SWPU (No. 2022QHZ023, 2022QHZ013), the Sichuan Science and Technology Program (No.2024YFHZ0022), the Sichuan Scientific Innovation Fund (No. 2022JDRC0009), the Sichuan Provincial Department of Science and Technology Project (No. 2022NSFSC0283), the Key Research and Development Project of Sichuan Provincial Department of Science and Technology (No.2023YFG0129). In addition, we also thank the High-Performance Computing Center, Southwest Petroleum University for its support.

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Author notes

  1. Xingpeng Zhang, Jing Xu and Dong He are contributed equally to this work.

Authors and Affiliations

  1. School of Computer Science and Software Engineering, Southwest Petroleum University, Xindu road no. 8, Chengdu, 610500, China

    Xingpeng Zhang, Jing Xu & Dong He

  2. School of Computer Science and Technology, Sichuan University, No.2, Chuanda Road, Chengdu, 610207, China

    Kaixin Wang

  3. Department of Computer Center, Zigong Fourth People’s Hospital, No. 400 Dangui Street, Zigong, 643000, China

    Liping Wang

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  1. Xingpeng Zhang
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Contributions

X.Z., J.X., and D.H. conceived of the idea and developed the proposed approaches. K.W. helped edit the paper. L.W. improved the quality of the manuscript and the completed revision. All authors reviewed the manuscript.

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Correspondence to Xingpeng Zhang.

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Zhang, X., Xu, J., He, D. et al. Lightweight multi-scale attention group fusion structure for nuclei segmentation. J Supercomput 81, 199 (2025). https://doi.org/10.1007/s11227-024-06710-9

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