TriCD-Net: Triple-Attention Coordinated Cross-Layer Dynamic Network for Few-Shot Medical Image Segmentation

Few-shot Medical Image Segmentation (FSMIS) has garnered increasing attention for its ability to reduce reliance on large-scale pixel-wise annotations. However, most existing methods rely solely on single-level encoder outputs, neglecting the complementary roles of hierarchical features in contour refinement and regional discrimination. This limitation often results in boundary ambiguity and local segmentation errors. To this end, we propose a Triple-Attention Coordinated Cross-layer Dynamic Network (TriCD-Net) for FSMIS. Specifically, we design a Feature-Enhanced Boundary Refinement (FEBR) module in each encoder layer, where three parallel attention branches explicitly model foreground, background, and boundary regions. The resulting features are passed to a Dense Dual-Contrast Segmentation (DDCS) head to perform segmentation, enabling progressive boundary refinement. Considering that query images inherently contain valuable structural information, we introduce a mask reconstruction task and design a Cross-layer Dynamic Convolution Fusion (CDCF) module that dynamically generates convolution kernels to adaptively fuse multi-level features for query reconstruction. Furthermore, we establish an interaction between the reconstruction and segmentation branches through an Uncertainty-Gated Cross-Attention Injection (UCI) module, which selectively injects reconstruction features into the segmentation branch to enhance structural coherence and boundary accuracy. Extensive experiments on three public benchmark datasets demonstrate that TriCD-Net consistently achieves state-of-the-art performance. The code is available at https://github.com/qchi-code/TriCD-Net.