Joint Segmentation of Primary Nasopharyngeal Carcinoma Tumors and Lymph Nodes via Global Attention

Nasopharyngeal carcinoma (NPC) is a malignant tumor whose accurate segmentation is a prerequisite for patient treatment and prognosis. Location and area of lymph nodes (LN) provide significant information for patient staging. However, existing methods for segmenting primary NPC tumors and LNs ignore three major challenges, i.e., uncertain position, irregular boundary, and false negative, thus, obtaining unsatisfactory segmentation results. Given the remarkable success of capturing global context information in Transformer, we have tailored a Transformer-based architecture, named NPCTrans, to address these limitations. NPCTrans is underpinned by three critical modules, including the position locating module (PLM), the boundary attention module (BAM), and the lesion region correction module (LRC). The PLM aims to improve sensitivity towards lesion regions by utilizing relative position encodings and control position information by gated in global attention. The BAM captures and refines the key points of the irregular boundary to enhance boundary segmentation performance. The LRC focuses on multi-scale feature learning by augmenting features of pixels and regions of interest at different scales to correctly identify multiple lesion regions to reduce false negatives. Extensive benchmarking experiments are conducted on our benchmarking dataset, which includes 9,100 samples collected from 723 patients, and results demonstrate that NPCTrans outperforms existing state-of-the-art models.