A self-supervised depth-aware method for pose optimization in hybrid bronchoscopic navigation.

Pose optimization is essential for aligning bronchoscopic views with preoperative CT scans in hybrid vision-electromagnetic navigation systems. It helps eliminate the discrepancies between the virtual and actual bronchoscope positions in the navigation process. However, a large modality gap between virtual (CT-rendered) and real bronchoscopic scenes introduces appearance and scale ambiguities that hinder robust pose estimation. This paper presents a self-supervised pose estimation method, named Depth-aware Attention Pose Network (DaAPNet). In particular, DaAPNet integrates a Depth-aware Attention module to resolve structural ambiguities by leveraging depth information to guide attention towards geometrically consistent regions, such as bronchial bifurcations. To further suppress unreliable appearance cues, an entropy-based uncertainty-weighted masking mechanism dynamically attenuates low-confidence regions in the attention process. A dedicated scale prediction module further enforces geometric consistency to recover cross-modality scale factor, while a pose consistency loss promotes temporal stability. Extensive experiments on virtual and phantom bronchus datasets show that DaAPNet achieves improved estimated performance. Qualitative results indicate that it effectively mitigates appearance ambiguity by capturing clear representations of bronchial bifurcations. Quantitatively, it restores scale consistency along the forward direction, reducing translation error by 35.99%.