基于解剖引导与隐式神经表示注意力的鼻腔分割OA

This paper proposes a novel medical image segmentation framework—Anatomy-Guided Implicit Re-presentation 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 Ua-Net.