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<p>Early detection of lung cancer is critical for improving treatment outcomes, and automatic lung image segmentation plays a key role in diagnosing lung-related diseases such as cancer, COVID-19, and respiratory disorders. Challenges include overlapping anatomical structures, complex pixel-level feature fusion, and intricate morphology of lung tissues all of which impede segmentation accuracy. To address these issues, this paper introduces GEANet, a novel framework for lung segmentation in CT images. GEANet utilizes an encoder-decoder architecture enriched with radiomics-derived features. Additionally, it incorporates Graph Neural Network (GNN) modules to effectively capture the complex heterogeneity of tumors. Additionally, a boundary refinement module is incorporated to improve image reconstruction and boundary delineation accuracy. The framework utilizes a hybrid loss function combining Focal Loss and IoU Loss to address class imbalance and enhance segmentation robustness. Experimental results on benchmark datasets demonstrate that GEANet outperforms eight state-of-the-art methods across various metrics, achieving superior segmentation accuracy while maintaining computational efficiency.</p>

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Graph neural network model using radiomics for lung CT image segmentation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月1日

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  • Mohammad Khalid Faizi,
  • Yan Qiang,
  • Md Masum Billa Shagar,
  • Yangyang Wei,
  • Ying Qiao,
  • Juanjuan Zhao,
  • Zia Urrehman

摘要

Early detection of lung cancer is critical for improving treatment outcomes, and automatic lung image segmentation plays a key role in diagnosing lung-related diseases such as cancer, COVID-19, and respiratory disorders. Challenges include overlapping anatomical structures, complex pixel-level feature fusion, and intricate morphology of lung tissues all of which impede segmentation accuracy. To address these issues, this paper introduces GEANet, a novel framework for lung segmentation in CT images. GEANet utilizes an encoder-decoder architecture enriched with radiomics-derived features. Additionally, it incorporates Graph Neural Network (GNN) modules to effectively capture the complex heterogeneity of tumors. Additionally, a boundary refinement module is incorporated to improve image reconstruction and boundary delineation accuracy. The framework utilizes a hybrid loss function combining Focal Loss and IoU Loss to address class imbalance and enhance segmentation robustness. Experimental results on benchmark datasets demonstrate that GEANet outperforms eight state-of-the-art methods across various metrics, achieving superior segmentation accuracy while maintaining computational efficiency.