Bridge-Net: Boundary-Ambiguity Guided Residual Injection for MRI-Based Brain Tumor Segmentation

Encoder-decoder segmentation networks use skip connections to recover spatial detail, but direct feature transfer can also propagate irrelevant high-frequency responses into the decoder. This problem is pronounced at weak or irregular tumor margins, where contour evidence is useful but should not modify semantically reliable features indiscriminately. We propose Bridge-Net, a boundary-ambiguity guided residual injection framework for two-dimensional brain tumor MRI segmentation. Bridge-Net formulates boundary enhancement as a conditional feature-correction problem. A structural boundary prior is coupled multiplicatively with an ambiguity map obtained from an auxiliary foreground logit. The ambiguity cue is deterministic and probability-based, rather than a Bayesian or calibrated uncertainty estimate. The overlap between the two cues identifies locations that are both boundary-like and prediction-ambiguous. A level-specific gated residual branch then injects the resulting cue into each skip feature, while a zero-initialized bounded scaling factor preserves an identity-like main pathway at the start of optimization. Experiments were conducted on TCGA-LGG and BRISC2025 under dataset-specific protocols, including patient-level partitioning for TCGA-LGG and the official image-level split for BRISC2025. Bridge-Net achieved Dice/IoU/HD95 values of 84.16%/73.65%/15.38 on TCGA-LGG and 87.26%/77.63%/8.68 on BRISC2025. Patient-level paired analysis on TCGA-LGG and image-level paired analysis on BRISC2025 further supported the improvements over UCTransNet. Ablation results support the complementary roles of structural boundary and probability-ambiguity cues, and a same-seed repeated-run comparison across reproduced models supports the stability of the observed performance trend under the tested setting. Relative to UCTransNet, the proposed mechanism increases the parameter count from 7.982 M to 7.988 M and FLOPs from 24.083 G to 24.196 G at 256×256 resolution. These results indicate that ambiguity-filtered boundary residual fusion introduces only a small increase in parameter count and FLOPs for boundary-ambiguous MRI segmentation, although broader patient-level and volumetric validation remains necessary.