PF-Mamba-UNet: An Anatomical-Prior-Guided Medical Image Segmentation Network Based on Mamba and Spatial-Channel Collaborative Attention Fusion

Medical image segmentation plays a pivotal role in clinical diagnosis assistance, lesion detection, and computer-aided medicine. However, factors such as complex organ boundary morphologies, multi-scale structural variations, and low contrast continue to pose challenges for accurate segmentation. To address this, this paper proposes an efficient segmentation network based on the fusion of anatomical prior and features—Anatomical-prior-guided Fusion Mamba-UNet (PF-Mamba-UNet). Firstly, the CR-VSS module is designed. Within the VSS module, a convolutional refinement branch is constructed to enhance local detail modeling. An integrated CSS2D module enables multi-directional scanning along horizontal, vertical, diagonal, and anti-diagonal paths, boosting the model’s multi-directional perception and feature capture capabilities for complex geometries. Secondly, the Coordinated Spatial-Channel Fusion Attention (CSCFA) module is designed and embedded in the skip-connection layers of the encoder and decoder. Through a spatial-first mechanism, it synergistically enhances spatial and channel features, thereby highlighting critical anatomical regions while suppressing redundant background noise. Finally, an anatomical prior regularization is constructed to balance boundary smoothing and regional consistency. The former ensures continuity and smoothness of segmentation edges, while the latter guarantees structural consistency within regions. Collectively, they ensure the anatomical plausibility and structural continuity of segmentation results. Medical image segmentation results demonstrate that PF-Mamba-UNet exhibits significant performance advantages over mainstream models including U-Net, SwinUNet, Mamba-UNet, Mamba-Sea, KM-Unet and CSWin-Unet.