Weighted Fusion Transformer for Dual PET/CT Head and Neck Tumor Segmentation

Abstract

Accurate tumor segmentation in PET/CT imaging is essential for the diagnosis and treatment of cancer, impacting therapeutic outcomes and patient management. Our study introduces a new approach integrating a Weighted Fusion Transformer Network to enhance the segmentation of tumor volumes. This method synergizes PET and CT modalities through a Fusion FormerU-Net architecture that employs convolutional neural networks alongside transformer blocks, aiming to leverage the unique advantages of each imaging modality. We evaluated the proposed approach using a multi-institutional dataset, applying key performance metrics such as Dice Similarity Coefficient aggregate, Jaccard Index, Volume Correlation, and Average Surface Distance to assess segmentation precision. The results indicate that the CT/PET/Fusion strategy significantly improves tumor delineation, outperforming traditional segmentation methods. The main findings suggest that this integrative approach could potentially redefine the standard for tumor segmentation in clinical practice. Lastly, the proposed approach offers a promising direction for enhancing the accuracy of oncological imaging, with implications for the improvement of patient-specific treatment strategies.

Keywords

• PET/CT imaging
• tumor segmentation
• weighted fusion transformer
• multi-modal imaging
• deep learning
• neural networks