MBRSNet: Boundary-Aware Multi-Task Learning with Signed Distance Field Regression for Polyp Segmentation

Accurate polyp segmentation in colonoscopic images remains challenging due to low contrast, irregular morphology, and significant distribution shifts across datasets, which often lead to unreliable boundary delineation and poor generalization. Existing methods typically treat boundary information as an auxiliary cue or incorporate boundary information through hand-crafted architectural designs, resulting in limited integration between boundary-sensitive features and region-aware representations. In this paper, we propose a boundary-aware multi-task learning framework, termed MBRSNet, which explicitly models and exploits the complementarity between the segmentation task and the auxiliary signed distance field (SDF) regression task. Specifically, we formulate boundary modeling as an auxiliary SDF regression task, providing dense and continuous structural supervision without requiring additional annotations. To effectively couple the two tasks, we design a cross-gated multi-task bottleneck that enables bidirectional and selective feature interaction, allowing each task to selectively leverage complementary information while suppressing task-irrelevant responses. Furthermore, a hierarchical cross-task guidance strategy is introduced in the decoding stage, where boundary-aware weighting and segmentation-guided alignment jointly refine multi-scale features, ensuring consistent integration of boundary cues and regional semantics. Extensive experiments on five benchmark datasets demonstrate that MBRSNet achieves competitive or superior performance compared with representative state-of-the-art methods in both segmentation accuracy and cross-dataset generalization. In particular, the proposed framework achieves superior boundary delineation under challenging conditions and exhibits strong robustness to domain shifts, highlighting the effectiveness of structured task interaction for boundary-aware medical image segmentation.