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<p>To improve the precision of medical image segmentation for enhanced clinical diagnosis and treatment, this study focuses on overcoming the limitations of existing models in capturing multi-scale information under resolution constraints while maintaining efficiency without compromising accuracy. We developed a multi-scale dense dilated Transformer (MDFormer), which integrates the multi-scale dense dilated self-attention (MDDSA) module. This module dynamically adjusts the size of the dense dilated matrix based on the resolution characteristics, enabling spatial downsampling at a fixed resolution and cross-scale information aggregation to effectively reduce computational costs. Our model achieved DSC of 92.7% on the ACDC dataset and 86.88% on the Synapse dataset, with 37.7&#xa0;M parameters and 47.39G FLOPs. Paired T-tests were performed, demonstrating statistically significant improvements in segmentation performance compared to other models. The proposed MDFormer significantly enhances medical image segmentation through the effective leveraging of multi-scale information extraction, demonstrating promising potential in various medical image segmentation tasks and laying a solid foundation for future advancements in transformer-based models.</p>

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MDFormer: a multi-scale dense dilated transformer model for 3D medical image segmentation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月30日

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  • Qiuxia Li,
  • Haiyue Zhao,
  • Ying Zhai,
  • Fangzuo Li

摘要

To improve the precision of medical image segmentation for enhanced clinical diagnosis and treatment, this study focuses on overcoming the limitations of existing models in capturing multi-scale information under resolution constraints while maintaining efficiency without compromising accuracy. We developed a multi-scale dense dilated Transformer (MDFormer), which integrates the multi-scale dense dilated self-attention (MDDSA) module. This module dynamically adjusts the size of the dense dilated matrix based on the resolution characteristics, enabling spatial downsampling at a fixed resolution and cross-scale information aggregation to effectively reduce computational costs. Our model achieved DSC of 92.7% on the ACDC dataset and 86.88% on the Synapse dataset, with 37.7 M parameters and 47.39G FLOPs. Paired T-tests were performed, demonstrating statistically significant improvements in segmentation performance compared to other models. The proposed MDFormer significantly enhances medical image segmentation through the effective leveraging of multi-scale information extraction, demonstrating promising potential in various medical image segmentation tasks and laying a solid foundation for future advancements in transformer-based models.