BEMF-Net: A Boundary-Enhanced Multi-Scale Feature Fusion Network

The elevated morbidity and mortality of kidney cancer make the precise, automated segmentation of kidneys and tumors essential for supporting clinical diagnosis and guiding surgical interventions. Recently, the segmentation of kidney tumors has been significantly advanced by deep learning. However, persistent challenges include the fuzzy boundaries of kidney tumors, multi-scale problems with kidney and renal tumors regarding location and size, and the strikingly similar textural characteristics of malignant lesions and the surrounding renal parenchyma. To overcome the aforementioned constraints, this study introduces a boundary-enhanced multi-scale feature fusion network (BEMF-Net) for endoscopic image segmentation of kidney tumors. This network incorporates a boundary-selective attention module (BSA) to cope with the renal tumor boundary ambiguity problem and obtain more accurate tumor boundaries. Furthermore, we introduce a multi-scale feature fusion attention module (MFA) designed to handle 4 distinct feature hierarchies captured by the encoder, enabling it to effectively accommodate the diverse size variations observed in kidney tumors. Finally, a hybrid cross-modal attention module (HCA) is introduced to conclude our design. It is designed with a dual-branch structure combining Transformer and CNN, thereby integrating both global contextual relationships and fine-grained local patterns. On the Re-TMRS dataset, our approach achieved mDice and mIoU scores of 91.2% and 85.7%. These results confirm its superior segmentation quality and generalization performance compared to leading existing methods.