Endobronchial intervention is increasingly used as a minimally invasive means for the treatment of pulmonary diseases. In order to reduce the difficulty of manipulation in complex airway networks, robust lumen detection is essential for intraoperative guidance. However, these methods are sensitive to visual artifacts which are inevitable during the surgery. In this work, a cross domain feature interaction (CDFI) network is proposed to extract the structural features of lumens, as well as to provide artifact cues to characterize the visual features. To effectively extract the structural and artifact features, the Quadruple Feature Constraints (QFC) module is designed to constrain the intrinsic connections of samples with various imaging-quality. Furthermore, we design a Guided Feature Fusion (GFF) module to supervise the model for adaptive feature fusion based on different types of artifacts. Results show that the features extracted by the proposed method can preserve the structural information of lumen in the presence of large visual variations, bringing much-improved lumen detection accuracy.

翻译：支气管腔内介入手术正日益成为治疗肺部疾病的微创手段。为降低复杂气道网络中的操作难度，术中引导所需的鲁棒性管腔检测至关重要。然而，现有方法对手术过程中不可避免的视觉伪影较为敏感。本文提出一种跨域特征交互（CDFI）网络，用于提取管腔结构特征并提供伪影线索以表征视觉特征。为有效提取结构特征与伪影特征，设计了四重特征约束（QFC）模块，用于约束不同成像质量样本间的内在关联；同时，设计了引导式特征融合（GFF）模块，基于不同类型伪影监督模型进行自适应特征融合。实验结果表明，该方法提取的特征能够在较大视觉变化下保留管腔结构信息，显著提升管腔检测精度。