  Real-time 6 DOF localization of bronchoscopes is crucial for enhancing
intervention quality. However, current vision-based technologies struggle to
balance between generalization to unseen data and computational speed. In this
study, we propose a Depth-based Dual-Loop framework for real-time Visually
Navigated Bronchoscopy (DD-VNB) that can generalize across patient cases
without the need of re-training. The DD-VNB framework integrates two key
modules: depth estimation and dual-loop localization. To address the domain gap
among patients, we propose a knowledge-embedded depth estimation network that
maps endoscope frames to depth, ensuring generalization by eliminating
patient-specific textures. The network embeds view synthesis knowledge into a
cycle adversarial architecture for scale-constrained monocular depth
estimation. For real-time performance, our localization module embeds a fast
ego-motion estimation network into the loop of depth registration. The
ego-motion inference network estimates the pose change of the bronchoscope in
high frequency while depth registration against the pre-operative 3D model
provides absolute pose periodically. Specifically, the relative pose changes
are fed into the registration process as the initial guess to boost its
accuracy and speed. Experiments on phantom and in-vivo data from patients
demonstrate the effectiveness of our framework: 1) monocular depth estimation
outperforms SOTA, 2) localization achieves an accuracy of Absolute Tracking
Error (ATE) of 4.7 $\pm$ 3.17 mm in phantom and 6.49 $\pm$ 3.88 mm in patient
data, 3) with a frame-rate approaching video capture speed, 4) without the
necessity of case-wise network retraining. The framework's superior speed and
accuracy demonstrate its promising clinical potential for real-time
bronchoscopic navigation.
