鼻咽癌（NPC）是主要通过放射治疗治疗的恶性肿瘤。准确勾画靶肿瘤对于提高放疗效果至关重要。然而，由于边界较差、肿瘤体积变化大以及放射治疗手动勾画的劳动密集型性质，当前模型的分割性能并不令人满意。在本文中，MMCA-Net 是一种使用 PET/CT 图像的 NPC 新型分割网络，它结合了创新的多模态交叉注意变换器 (MCA-Transformer) 和改进的 U-Net 架构，通过利用跨模态融合来增强模态融合。 CT 和 PET 数据之间的注意力机制。我们的方法通过对来自中山大学癌症中心和公共 HECKTOR 数据集的样本进行五重交叉验证，对十种算法进行了测试，始终领先于所有四个评估指标，平均 Dice 相似系数分别为 0.815 和 0.7944。此外，进行消融实验以证明我们的方法相对于多种基线和变体技术的优越性。所提出的方法在其他任务中具有广阔的应用潜力。

Nasopharyngeal carcinoma (NPC) is a malignant tumor primarily treated by radiotherapy. Accurate delineation of the target tumor is essential for improving the effectiveness of radiotherapy. However, the segmentation performance of current models is unsatisfactory due to poor boundaries, large-scale tumor volume variation, and the labor-intensive nature of manual delineation for radiotherapy. In this paper, MMCA-Net, a novel segmentation network for NPC using PET/CT images that incorporates an innovative multimodal cross attention transformer (MCA-Transformer) and a modified U-Net architecture, is introduced to enhance modal fusion by leveraging cross-attention mechanisms between CT and PET data. Our method, tested against ten algorithms via fivefold cross-validation on samples from Sun Yat-sen University Cancer Center and the public HECKTOR dataset, consistently topped all four evaluation metrics with average Dice similarity coefficients of 0.815 and 0.7944, respectively. Furthermore, ablation experiments were conducted to demonstrate the superiority of our method over multiple baseline and variant techniques. The proposed method has promising potential for application in other tasks.