CDFI: Cross Domain Feature Interaction for Robust Bronchi Lumen Detection
This work addresses a domain-specific problem for medical imaging in pulmonary surgery, offering an incremental improvement over existing methods.
The paper tackled robust bronchi lumen detection in endobronchial interventions by proposing a cross-domain feature interaction network with modules for structural and artifact feature extraction, resulting in much-improved detection accuracy in the presence of visual artifacts.
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.