DCLCA-UNet: Dynamic Cross-Level Contextual Attention for 3D Medical Image Segmentation

Accurate 3D medical image segmentation requires effective integration of encoder details and decoder semantics. However, their relative contributions vary across decoding stages and spatial locations, and attention based only on the current feature pair may provide limited guidance in ambiguous regions. To address these challenges, we propose Dynamic Cross-Level Contextual Attention U-Net (DCLCA-UNet), which reconstructs cross-level context at each decoding stage from the incoming decoder feature and encoder features at the corresponding and shallower levels. The Cross-Level Contextual Attention Fusion (CLCAF) module uses this context to adaptively weight encoder and decoder features before fusion. The Context-Guided Feature Modulation (CGFM) module then reuses the context to refine the fused representation through scaling and additive correction. Experiments on Synapse, BTCV, and BraTS2023 yield average Dice scores of 88.70%, 84.13%, and 92.34%, respectively, with the lowest corresponding average Hausdorff distance (HD) or 95th-percentile Hausdorff distance (HD95) among the compared methods. These improvements are accompanied by increased computational cost. Repeated experiments and evaluation on AMOS22 provide additional evidence of stability and cross-dataset generalization. Ablation studies further assess the contributions of contextual fusion, modulation, and stage-specific context reconstruction. Together, these results support the effectiveness of contextual guidance in volumetric segmentation.