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<p>Medical image segmentation plays a crucial role in improving diagnostic accuracy and therapeutic strategies. Traditional methods often struggle with noise interference and blurred boundaries in medical images. This study introduces a novel segmentation network, termed wavelet-deformable attention network (WDANet), featuring a wavelet-enhanced deformable attention decoder (WEDA). WEDA integrates a wavelet-deformable convolutional block (WDCB) and a multi-scale frequency space fusion block (MSFB) to enhance feature extraction and contextual awareness. The WDCB combines wavelet-deformable convolution with a convolutional attention mechanism, while the MSFB jointly models multi-scale spatial representations and DCT-based frequency attention to enhance structural feature learning. Experiments on the ACDC and Synapse datasets demonstrate that WDANet achieves average Dice scores of 92.53% and 85.67%, respectively, outperforming existing state-of-the-art methods. These results validate the effectiveness of WDANet in medical image segmentation applications. The code is publicly available at <a href="https://github.com/zhangxuan-thecastle/WDANet">https://github.com/zhangxuan-thecastle/WDANet</a>.</p>

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Enhanced medical image segmentation via wavelet-deformable attention networks

  • 期刊论文

  • 发表日期：

    2026年5月2日

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  • Xuan Zhang,
  • Rui Liu,
  • Jing Dong,
  • Pengfei Yi,
  • Xiaopeng Wei

摘要

Medical image segmentation plays a crucial role in improving diagnostic accuracy and therapeutic strategies. Traditional methods often struggle with noise interference and blurred boundaries in medical images. This study introduces a novel segmentation network, termed wavelet-deformable attention network (WDANet), featuring a wavelet-enhanced deformable attention decoder (WEDA). WEDA integrates a wavelet-deformable convolutional block (WDCB) and a multi-scale frequency space fusion block (MSFB) to enhance feature extraction and contextual awareness. The WDCB combines wavelet-deformable convolution with a convolutional attention mechanism, while the MSFB jointly models multi-scale spatial representations and DCT-based frequency attention to enhance structural feature learning. Experiments on the ACDC and Synapse datasets demonstrate that WDANet achieves average Dice scores of 92.53% and 85.67%, respectively, outperforming existing state-of-the-art methods. These results validate the effectiveness of WDANet in medical image segmentation applications. The code is publicly available at https://github.com/zhangxuan-thecastle/WDANet.