Query Learning of Both Thing and Stuff For Panoptic Segmentation

Nasopharyngeal carcinoma (NPC) is a malignant tumor whose accurate segmentation is a prerequisite for treatment. However, existing deep learning methods achieve unsatisfactory segmentation performance on NPC MR images, since NPC is infiltrative with small ambiguous boundary volume, making it indiscernible from tightly connected surrounding and complex backgrounds. To address the issues, a NPC segmentation network, termed NPCFormer, is proposed. The NPCFormer consists of two modules, Skip Residual Transformer (SRT) and Boundary Attention Unit (BAU), which are designed for NPC segmentation. The two modules are proposed via redesigning the multi-head self-attention to achieve accurate segmentation of NPC. The SRT exploits the global position context for the NPC locating. The BAU discriminates the tumor boundaries from its surrounding tissues by utilizing the global position context. Extensive experiments on our dataset demonstrate the proposed NPCFormer could distinguish and segment NPC from complex background tissues accurately.