CVP-BAA: Efficiently Adapting Segment Anything Model for Medical Image Segmentation

Overview & Synopsis

Various task-specific medical segmentation models have demonstrated strong performance, their heavy reliance on extensive annotations and highly specialized architectures limits their versatility, scalability, and clinical applicability. Recently, adapting vision foundation models, particularly the segment anything model (SAM), has emerged as a promising alternative. However, prevalent parameter-efficient fine-tuning (PEFT) strategies, such as prompt tuning and adapter tuning, often lack an explicit mechanism to embed local structural priors into the long-range dependency modeling of vision transformers (ViT), rendering them insufficiently sensitive to fine-grained details. To address these limitations, we introduce CVP-BAA, a novel hybrid PEFT method that improves SAM's sensitivity to domain-specific textures and local details, leading to superior boundary delineation. The proposed CVP-BAA adopts two key ideas. First, it introduces a convolutional visual prompting (CVP) strategy that leverages convolutional features as structural priors, prepending them to the key and value projections within the multi-head self-attention layers of strategically selected ViT blocks. Through a dedicated prompt generation-and-injection module, this mechanism effectively bridges local spatial details with global context semantics. Second, a boundary-aware adapter (BAA) is designed as a parallel branch to the feed-forward network within the same selected blocks. By explicitly computing and amplifying high-frequency components, the BAA enhances feature representations along tissue boundaries and fine-grained lesion structures. Extensive experiments on five benchmarks across three imaging modalities demonstrate that CVP-BAA achieves superior segmentation performance, surpassing existing task-specific models and PEFT-based SAM adaptations in both quantitative metrics and qualitative results.