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<p>Medical image segmentation is crucial for accurate diagnosis and treatment planning. Traditional methods struggle with complex medical images, while recent deep learning advancements, particularly U-Net and its variants, often suffer from insufficient feature extraction and misalignment issues. This paper introduces MA-UNet, an enhanced segmentation network based on U-Net, which integrates multi-scale features and a hybrid attention mechanism. MA-UNet is designed with a two-stage encoder comprising rough ordinary extraction and multi-scale fine extraction to improve feature representation. A hybrid feature optimization module is embedded in skip connections to address feature misalignment. Additionally, the Transformer in the bottleneck layer is optimized, and multi-scale convolution is utilized in the decoder. Experimental results on three datasets demonstrate that MA-UNet achieves optimal or near-optimal segmentation performance, outperforming ten state-of-the-art methods. The proposed framework significantly enhances medical image segmentation accuracy, potentially aiding clinicians in making more precise decisions. Our code is available at <a href="https://github.com/HZ-LL/MA-UNet">https://github.com/HZ-LL/MA-UNet</a>.</p>

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Enhancing medical image segmentation with MA-UNet: a multi-scale attention framework

  • 期刊论文

  • 发表日期：

    2025年1月5日

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  • Hongzhi Li,
  • Zhanghao Ren,
  • Guoqing Zhu,
  • Yaoju Liang,
  • Han Cui,
  • Chaozeyu Wang,
  • Jiaxi Wang

摘要

Medical image segmentation is crucial for accurate diagnosis and treatment planning. Traditional methods struggle with complex medical images, while recent deep learning advancements, particularly U-Net and its variants, often suffer from insufficient feature extraction and misalignment issues. This paper introduces MA-UNet, an enhanced segmentation network based on U-Net, which integrates multi-scale features and a hybrid attention mechanism. MA-UNet is designed with a two-stage encoder comprising rough ordinary extraction and multi-scale fine extraction to improve feature representation. A hybrid feature optimization module is embedded in skip connections to address feature misalignment. Additionally, the Transformer in the bottleneck layer is optimized, and multi-scale convolution is utilized in the decoder. Experimental results on three datasets demonstrate that MA-UNet achieves optimal or near-optimal segmentation performance, outperforming ten state-of-the-art methods. The proposed framework significantly enhances medical image segmentation accuracy, potentially aiding clinicians in making more precise decisions. Our code is available at https://github.com/HZ-LL/MA-UNet.