Abstract
Lung cancer, as one of the most lethal malignancies worldwide, primarily relies on radiation therapy, with about 60%-70% of patients requiring this treatment. In radiation therapy planning, precise segmentation of the Gross Tumor Volume (GTV) in CT images is crucial. However, the low contrast between the tumor and surrounding tissues, small size of the tumor area, and high heterogeneity of its internal structure pose significant technical challenges for accurate segmentation. To address these limitations, we propose RAU-Net (ROI-Attention U-Net), a two-stage framework, which combines target detection for Region of Interest (ROI) localization with a refined U-Net architecture incorporating attention mechanisms. Experiments on Lung Cancer GTV Dataset1 demonstrated that RAU-Net achieved a Dice coefficient of (77.13 ± 0.55)% and a sensitivity of (80.38 ± 0.63)% on the validation set, representing improvements of 4.1% and 6.25%, respectively, compared to the next best model, and significantly outperforming traditional U-Net and other advanced models. Similarly, On Lung Cancer GTV Dataset2, RAU-Net demonstrated remarkable performance, achieving the highest Dice coefficient of (73.95 ± 0.66)% and the second-highest Sensitivity of (66.40 ± 0.92)%, showcasing its superiority over other models overall. Ablation studies further confirmed the crucial role of the ROI extraction phase, attention mechanism, SE-Res module, and Combined Loss Function (CLoss) in enhancing segmentation performance. This framework provides a clinically viable solution for GTV delineation while offering methodological insights for medical image analysis.