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

CDFI: Cross Domain Feature Interaction for Robust Bronchi Lumen Detection