Rethinking Feature Interactions for Medical Image Segmentation: A Unified Hierarchical Aggregation Framework With...

Medical image segmentation is a crucial task of medical image analysis and computer vision. Medical images, compared to natural ones, contain more complex semantic information, making feature learning more challenging. Existing encoder-decoder architectures are limited by inadequate cross-scale interaction and insufficient boundary modeling in their feature fusion designs. To address this, we propose a Hierarchical Feature Interaction network with Boundary guidance (HFIBNet), which unifies dynamic cross-level feature fusion and explicit edge supervision within a coarse-to-fine segmentation framework. Specifically, we introduce a Boundary Prediction (BP) module to extract boundary-aware features that guide the fusion process. A Cross-Level Feature Fusion (CLFF) module is designed to promote semantic interaction across adjacent encoder stages, while the Edge Feature Aggregation (EFA) module propagates boundary cues hierarchically to enhance structural consistency. Furthermore, a Partially Parallel Decoder (PPD) generates a coarse global prediction, which is progressively refined by a Global-Local Feature Enrichment (GLFE) module, mimicking the clinical annotation workflow from coarse localization to fine delineation. Extensive experiments on ten public medical segmentation datasets across four distinct tasks demonstrate that HFIBNet consistently outperforms existing state-of-the-art methods.