his paper proposes a novel medical image segmentation framework — Anatomy-Guided Implicit Representation Attention Network (AIRA-Net) — designed to address the challenges posed by the complex and variable anatomical structures of the nasal cavity and its subregions. AIRA-Net leverages an implicit neural representation to extract global geometric features and employs a dedicated cross-attention module to effectively fuse multi-scale local and global features. Furthermore, a boundary-weighted loss function based on anatomical priors is integrated to enhance segmentation precision in regions with sparse features, particularly at the cavity boundaries. Extensive experiments on a dataset comprising 128 3D head CT volumes demonstrate that AIRA-Net achieves a DSC of 91.66% in nasal cavity segmentation, surpassing the second-best method nnU-Net by 4.5 percentage points. Additionally, AIRA-Net attains a HD95 of 10.75 mm, which is 2.82 mm lower than that of the second-best method U_a-Net.

medical image segmentation, nasal cavity segmentation, implicit neural representation, cross-attention mechanism, anatomy-guided prior