SRWKV: A Shape-Guided RWKV With Adaptive Receptive Fields for Efficient Medical Image Segmentation

Existing medical image segmentation (MedISeg) models predominantly rely on convolutional neural networks (CNNs) and Transformer architectures. However, the limited receptive fields of CNNs and the quadratic computational cost of Transformers hinder their scalability and efficiency. Recently, receptance-weighted key-value (RWKV) has emerged as a promising linear-complexity alternative for global context modeling. In this article, we propose SRWKV, a shape-guided RWKV (SGR) model for parameter-efficient MedISeg. SRWKV introduces an SGR block that uses a shape prior predicted from the deepest encoder feature to guide token traversal during decoding, reducing foreground-background interleaving and improving structural coherence during sequence formation. In addition, we develop a deformable adaptive shift (DA-Shift) module that dynamically adjusts token interactions according to local context, enabling flexible receptive field adaptation for diverse anatomical structures. Extensive experiments across six MedISeg tasks on 11 datasets demonstrate that SRWKV achieves strong segmentation performance with a compact parameter footprint. Our code is available at https://github.com/ukeLin/SRWKV.