Radiotherapy (RT) combined with cetuximab is the standard treatment for patients with inoperable head and neck cancers. Segmentation of head and neck (H&N) tumors is a prerequisite for radiotherapy planning but a time-consuming process. In recent years, deep convolutional neural networks have become the de facto standard for automated image segmentation. However, due to the expensive computational cost associated with enlarging the field of view in DCNNs, their ability to model long-range dependency is still limited, and this can result in sub-optimal segmentation performance for objects with background context spanning over long distances. On the other hand, Transformer models have demonstrated excellent capabilities in capturing such long-range information in several semantic segmentation tasks performed on medical images. Inspired by the recent success of Vision Transformers and advances in multi-modal image analysis, we propose a novel segmentation model, debuted, Cross-Modal Swin Transformer (SwinCross), with cross-modal attention (CMA) module to incorporate cross-modal feature extraction at multiple resolutions.To validate the effectiveness of the proposed method, we performed experiments on the HECKTOR 2021 challenge dataset and compared it with the nnU-Net (the backbone of the top-5 methods in HECKTOR 2021) and other state-of-the-art transformer-based methods such as UNETR, and Swin UNETR. The proposed method is experimentally shown to outperform these comparing methods thanks to the ability of the CMA module to capture better inter-modality complimentary feature representations between PET and CT, for the task of head-and-neck tumor segmentation.

翻译：放射治疗（RT）联合西妥昔单抗是不可手术头颈部肿瘤患者的标准治疗方案。头颈部（H&N）肿瘤分割是放疗计划制定中的必要步骤，但这一过程耗时较长。近年来，深度卷积神经网络已成为自动化图像分割的事实标准。然而，由于深度卷积神经网络（DCNN）扩大感受野的计算成本较高，其建模长距离依赖关系的能力仍十分有限，这可能导致对跨越长距离背景上下文的物体分割性能欠佳。另一方面，Transformer模型在医学图像的语义分割任务中已展现出卓越的长距离信息捕获能力。受近期Vision Transformers的成功及多模态图像分析进展的启发，我们提出了一种新颖的分割模型——跨模态Swin Transformer（SwinCross），其核心为跨模态注意力（CMA）模块，能够在多分辨率尺度上整合跨模态特征提取。为验证所提方法的有效性，我们在HECKTOR 2021挑战数据集上进行了实验，并与nnU-Net（HECKTOR 2021排名前五方法的核心架构）以及其他基于Transformer的最新方法（如UNETR和Swin UNETR）进行了比较。实验结果表明，所提方法优于上述对比方法，这得益于CMA模块能够更好地捕获PET与CT之间的跨模态互补特征表示，从而提升了头颈部肿瘤分割任务的性能。