Early detection and accurate segmentation of colorectal polyps during colonoscopy are crucial for the prevention of colorectal cancer. However, automated polyp segmentation remains challenging because of high inter-class variance, complex intestinal backgrounds, and blurred boundaries. To address these issues while maintaining computational efficiency, DSF-BRNet was developed for endoscopic polyp segmentation. In this framework, a Dual-Gated Semantic Fusion (DSF) module is introduced to reduce spatial misalignment between cross-level features and to provide a more reliable semantic basis for lesion localization. To further alleviate boundary ambiguity, a High-Frequency Boundary Refinement (HBR) module is used to sharpen segmentation contours under aligned semantic guidance. Together, these components form an Align-then-Refine framework in which semantic localization is strengthened before boundary refinement is performed. Experiments on four public benchmark datasets—Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, and ETIS-LaribPolypDB—showed competitive performance with favorable computational efficiency. Mean Dice scores of 0.943 on CVC-ClinicDB and 0.818 on ETIS-LaribPolypDB were achieved, with 25.55 M parameters and an inference speed of 80.08 FPS. These results indicate that accurate semantic localization and fine boundary preservation can be achieved simultaneously, suggesting that the method may be promising for real-time computer-aided diagnosis (CAD).
Liu et al. (Tue,) studied this question.