SwinCross: Cross‐modal Swin transformer for head‐and‐neck tumor segmentation in PET/CT images.

Background: 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 (DCNN) 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. Purpose: Despite the impressive representation capacity of vision transformer models, current vision transformer‐based segmentation models still suffer from inconsistent and incorrect dense predictions when fed with multi‐modal input data. We suspect that the power of their self‐attention mechanism may be limited in extracting the complementary information that exists in multi‐modal data. To this end, 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. Methods: We propose a novel architecture for cross‐modal 3D semantic segmentation with two main components: (1) a cross‐modal 3D Swin Transformer for integrating information from multiple modalities (PET and CT), and (2) a cross‐modal shifted window attention block for learning complementary information from the modalities. To evaluate the efficacy of our approach, we conducted experiments and ablation studies on the HECKTOR 2021 challenge dataset. We compared our method against nnU‐Net (the backbone of the top‐5 methods in HECKTOR 2021) and other state‐of‐the‐art transformer‐based models, including UNETR and Swin UNETR. The experiments employed a five‐fold cross‐validation setup using PET and CT images. Results: Empirical evidence demonstrates that our proposed method consistently outperforms the comparative techniques. This success can be attributed to the CMA module's capacity to enhance inter‐modality feature representations between PET and CT during head‐and‐neck tumor segmentation. Notably, SwinCross consistently surpasses Swin UNETR across all five folds, showcasing its proficiency in learning multi‐modal feature representations at varying resolutions through the cross‐modal attention modules. Conclusions: We introduced a cross‐modal Swin Transformer for automating the delineation of head and neck tumors in PET and CT images. Our model incorporates a cross‐modality attention module, enabling the exchange of features between modalities at multiple resolutions. The experimental results establish the superiority of our method in capturing improved inter‐modality correlations between PET and CT for head‐and‐neck tumor segmentation. Furthermore, the proposed methodology holds applicability to other semantic segmentation tasks involving different imaging modalities like SPECT/CT or PET/MRI. Code:https://github.com/yli192/SwinCross_CrossModalSwinTransformer_for_Medical_Image_Segmentation [ABSTRACT FROM AUTHOR]