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<p>In this study, a pure convolutional UNet with channel shuffle average, abbreviated as PC-UNet, has been proposed for medical image segmentation. Notably, the proposed PC-UNet is suitable for extracting context features, which is useful for model improvement. PC-UNet operates as an encoder-decoder network, where both the encoder and decoder are stacked with the proposed Pure Convolution (PC) modules. The PC module, containing a Channel Shuffle Average (CSA) component, is efficient in capturing context features without significant computational overhead. The CSA component transfers feature information from the channel dimension to the spatial dimension, enabling efficient computation. The effectiveness of the proposed PC-UNet has been rigorously validated on four widely used datasets, which are ISIC 2018, BUSI, GlaS, and Kvasir-SEG. Experimental results demonstrate that PC-UNet yields outstanding performance without imposing a significant computational load or increasing floating-point operations (FLOPs). When compared with eight mainstream models across all datasets, PC-UNet achieves the highest scores in both Dice and IoU metrics. The source code is available at: <a href="https://github.com/lwwant2sleep/PC-UNet.">https://github.com/lwwant2sleep/PC-UNet.</a></p>

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PC-UNet: a pure convolutional UNet with channel shuffle average for medical image segmentation

  • 期刊论文

  • 发表日期：

    2025年9月9日

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  • Wei Liu,
  • Qian Dong,
  • Shiren Li,
  • Cong Wang,
  • Yongliang Xiong,
  • Guangguang Yang

摘要

In this study, a pure convolutional UNet with channel shuffle average, abbreviated as PC-UNet, has been proposed for medical image segmentation. Notably, the proposed PC-UNet is suitable for extracting context features, which is useful for model improvement. PC-UNet operates as an encoder-decoder network, where both the encoder and decoder are stacked with the proposed Pure Convolution (PC) modules. The PC module, containing a Channel Shuffle Average (CSA) component, is efficient in capturing context features without significant computational overhead. The CSA component transfers feature information from the channel dimension to the spatial dimension, enabling efficient computation. The effectiveness of the proposed PC-UNet has been rigorously validated on four widely used datasets, which are ISIC 2018, BUSI, GlaS, and Kvasir-SEG. Experimental results demonstrate that PC-UNet yields outstanding performance without imposing a significant computational load or increasing floating-point operations (FLOPs). When compared with eight mainstream models across all datasets, PC-UNet achieves the highest scores in both Dice and IoU metrics. The source code is available at: https://github.com/lwwant2sleep/PC-UNet.