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Background <p>Accurate delineation of Gross Tumor Volume (GTV) in lung cancer is critical for effective radiotherapy and surgical planning. However, segmentation of GTV in high-resolution CT images remains challenging, particularly when tumors are small or have indistinct boundaries.</p> Methods <p>We propose D-S-Net, a novel dual-stage strategy to enhance both the accuracy and efficiency of lung cancer GTV segmentation. In the first stage, a simplified detection network is used to locate candidate regions in high-resolution (512×512) CT slices, reducing input size and computational demand. In the second stage, a modified U-Net variant is applied to perform fine segmentation within the detected regions. The architecture incorporates a spatial attention mechanism and employs a combined loss function (binary cross-entropy and Dice loss) to address class imbalance.</p> Results <p>On the lung cancer GTV dataset, D-S-Net achieved a Dice coefficient of 78.52%, representing an improvement of 5.49% over SwinU-Net and outperforming several mainstream models. On the second dataset, D-S-Net reached a Dice coefficient of 86.56%, surpassing the second-best model by 13.19%. Ablation studies demonstrated that the detection stage, spatial attention, and combined loss function effectively improved performance, while computational complexity analysis confirmed the model’s efficiency.</p> Conclusion <p>The proposed D-S-Net offers a robust and efficient solution for the segmentation of lung cancer GTV in CT images. Its dual-stage design and attention-based enhancements contribute to both accuracy and computational gains, highlighting its potential for clinical applications in radiotherapy planning.</p>

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D-S-Net: an efficient dual-stage strategy for high-precision segmentation of gross tumor volumes in lung cancer CT images

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月27日

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  • Chen Yi,
  • Shaofeng Jiang,
  • Liangli Xiong,
  • Jun Yang,
  • Huanhuan Shi,
  • Qiliang Xiong,
  • Bo Hu,
  • Huaiwen Zhang

摘要

Background

Accurate delineation of Gross Tumor Volume (GTV) in lung cancer is critical for effective radiotherapy and surgical planning. However, segmentation of GTV in high-resolution CT images remains challenging, particularly when tumors are small or have indistinct boundaries.

Methods

We propose D-S-Net, a novel dual-stage strategy to enhance both the accuracy and efficiency of lung cancer GTV segmentation. In the first stage, a simplified detection network is used to locate candidate regions in high-resolution (512×512) CT slices, reducing input size and computational demand. In the second stage, a modified U-Net variant is applied to perform fine segmentation within the detected regions. The architecture incorporates a spatial attention mechanism and employs a combined loss function (binary cross-entropy and Dice loss) to address class imbalance.

Results

On the lung cancer GTV dataset, D-S-Net achieved a Dice coefficient of 78.52%, representing an improvement of 5.49% over SwinU-Net and outperforming several mainstream models. On the second dataset, D-S-Net reached a Dice coefficient of 86.56%, surpassing the second-best model by 13.19%. Ablation studies demonstrated that the detection stage, spatial attention, and combined loss function effectively improved performance, while computational complexity analysis confirmed the model’s efficiency.

Conclusion

The proposed D-S-Net offers a robust and efficient solution for the segmentation of lung cancer GTV in CT images. Its dual-stage design and attention-based enhancements contribute to both accuracy and computational gains, highlighting its potential for clinical applications in radiotherapy planning.