  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.
