Enhancing non-small cell lung cancer radiotherapy planning: A deep learning-based multi-modal fusion approach for accurate GTV segmentation

In the treatment of non-small cell lung cancer (NSCLC), radiotherapy is pivotal. However, manual segmentation of the gross target volume (GTV) is both time-consuming and prone to errors. This study presents an automated solution using deep learning-based multi -modal fusion for efficient GTV segmentation. The method integrates data from CT, and PET images, enhancing GTV segmentation precision. Key features include intensity normalization and random cropping, which ensure data integration and variety. The system's core is the 3D U -Net, extracting detailed features from CT images, and the DeepLab network managing multi -modal fusion. The 3D Unet-DeepLab model demonstrates superior performance with a Dice Similarity Coefficient (DSC) of 0.7502, outperforming the 3D ResSE-Unet. Additionally, the 3D Unet_4B model surpasses existing techniques, with a DSC of 0.7502 and a Hausdorff Distance 95 mm (HD95) of 19.78 mm. These results confirm the method's capability in improving GTV segmentation precision and spatial consistency. Automating segmentation through this innovative method not only refines tumor targeting but also minimizes potential damage to healthy tissues, offering implications for personalized radiotherapy planning and enhanced NSCLC treatment outcomes.