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

Autor: Mohammed A. Mahdi, Shahanawaj Ahamad, Sawsan A. Saad, Alaa Dafhalla, Rizwan Qureshi, Alawi Alqushaibi
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: IEEE Access, Vol 12, Pp 110905-110919 (2024)
Druh dokumentu: article
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2024.3439439
Popis: 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.
Databáze: Directory of Open Access Journals