北京大学学报（自然科学版）2026，Vol.62Issue(3)：487-498,12.DOI:10.13209/j.0479-8023.2026.004
基于解剖引导与隐式神经表示注意力的鼻腔分割
Nasal Cavity Segmentation Based on Anatomy-Guided Implicit Representation Attention
摘要
Abstract
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.关键词
医学图像分割/鼻腔分割/隐式神经表示/交叉注意力机制/解剖先验引导Key words
medical image segmentation/nasal cavity segmentation/implicit neural representation/cross-attention mechanism/anatomy-guided prior引用本文复制引用
卢毅,邱继宽,张亚男,刘俊秀,白相志..基于解剖引导与隐式神经表示注意力的鼻腔分割[J].北京大学学报（自然科学版）,2026,62(3):487-498,12.基金项目
国家自然科学基金(62271016)和北京市自然科学基金(L242130)资助 （62271016）
