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<p>Abdominal organ segmentation is a challenging task due to anatomical complexity and low contrast in medical images. To address this, we propose the MSAE-U-Net, a novel deep learning architecture designed for robust and precise abdominal segmentation. It integrates three key innovations: the Multi-Scale Deformable Convolutional Block Attention Module (MS-D-CBAM) for multi-scale attention, the Adaptive Global Context Refinement ASPP (AGCR-ASPP) for dynamic context refinement, and the Rectangular Self-Calibration Module (RCM) for spatial feature recalibration. These modules enhance both local detail and global context in segmentation. Evaluated on the Dresden Surgical Anatomy dataset, MSAE-U-Net achieved superior performance with a Dice Similarity Coefficient (DSC) of 93.13%, IoU of 92.01%, and a precision of 94.67%, outperforming existing models like U-Net, nnU-Net, and AbdomenNet. These results demonstrate the model’s potential for clinical applications, including preoperative planning and automated organ identification.</p>

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MSAE-Unet: a multiscale attention-enhanced U-Net network for abdominal cancer segmentation using deep learning

  • 期刊论文

  • 发表日期：

    2026年2月2日

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  • Faisal Hayat,
  • Zhao Yuqian

摘要

Abdominal organ segmentation is a challenging task due to anatomical complexity and low contrast in medical images. To address this, we propose the MSAE-U-Net, a novel deep learning architecture designed for robust and precise abdominal segmentation. It integrates three key innovations: the Multi-Scale Deformable Convolutional Block Attention Module (MS-D-CBAM) for multi-scale attention, the Adaptive Global Context Refinement ASPP (AGCR-ASPP) for dynamic context refinement, and the Rectangular Self-Calibration Module (RCM) for spatial feature recalibration. These modules enhance both local detail and global context in segmentation. Evaluated on the Dresden Surgical Anatomy dataset, MSAE-U-Net achieved superior performance with a Dice Similarity Coefficient (DSC) of 93.13%, IoU of 92.01%, and a precision of 94.67%, outperforming existing models like U-Net, nnU-Net, and AbdomenNet. These results demonstrate the model’s potential for clinical applications, including preoperative planning and automated organ identification.