R2Nav: Robust, Real-time Test Time Adaptation for Robot Assisted Endoluminal Navigation

Abstract

Robot assisted endoluminal intervention is an emerging tool for treating luminal lesions. Vision-based endoluminal navigation, particularly through video-CT registration, is a tangible way of obtaining absolute camera position information. By using pre-operative CT data, accurate endoscope localization can be achieved, without the need of additional tracking hardware intraoperatively. However, aligning preoperative CT with intraoperative domain remains a challenge. Although approaches such as style transfer have been explored, patient-specific textures and intra-operative artifacts can significantly complicate the task. To overcome these challenges, we propose R2Nav, a robust, real-time test time adaptation method for endoluminal navigation. R2Nav constructs a confidence buffer during the testing phase, refining the model only for frames with high uncertainty. We introduce a registration-augmented model refinement strategy, which enhances both accuracy and efficiency of the system by selecting relevant training samples from the virtual gallery. Additionally, we propose a novel warm-up strategy for the registration encoder during the initial testing phase, enabling the extraction of more robust features when the model is suboptimal. Extensive validation demonstrates that R2Nav outperforms the current state-of-the-art methods, offering significant advantages for real-time, intra-operative endoluminal navigation. Code is at: https://github.com/EndoluminalSurgicalVision-IMR/R2Nav.