An endoscopic chisel: intraoperative imaging carves 3D anatomical models.

PURPOSE: Preoperative imaging plays a pivotal role in sinus surgery where CTs offer patient-specific insights of complex anatomy, enabling real-time intraoperative navigation to complement endoscopy imaging. However, surgery elicits anatomical changes not represented in the preoperative model, generating an inaccurate basis for navigation during surgery progression. METHODS: We propose a first vision-based approach to update the preoperative 3D anatomical model leveraging intraoperative endoscopic video for navigated sinus surgery where relative camera poses are known. We rely on comparisons of intraoperative monocular depth estimates and preoperative depth renders to identify modified regions. The new depths are integrated in these regions through volumetric fusion in a truncated signed distance function representation to generate an intraoperative 3D model that reflects tissue manipulation RESULTS: We quantitatively evaluate our approach by sequentially updating models for a five-step surgical progression in an ex vivo specimen. We compute the error between correspondences from the updated model and ground-truth intraoperative CT in the region of anatomical modification. The resulting models show a decrease in error during surgical progression as opposed to increasing when no update is employed. CONCLUSION: Our findings suggest that preoperative 3D anatomical models can be updated using intraoperative endoscopy video in navigated sinus surgery. Future work will investigate improvements to monocular depth estimation as well as removing the need for external navigation systems. The resulting ability to continuously update the patient model may provide surgeons with a more precise understanding of the current anatomical state and paves the way toward a digital twin paradigm for sinus surgery.

OneSLAM to map them all: a generalized approach to SLAM for monocular endoscopic imaging based on tracking any point.

PURPOSE: Monocular SLAM algorithms are the key enabling technology for image-based surgical navigation systems for endoscopic procedures. Due to the visual feature scarcity and unique lighting conditions encountered in endoscopy, classical SLAM approaches perform inconsistently. Many of the recent approaches to endoscopic SLAM rely on deep learning models. They show promising results when optimized on singular domains such as arthroscopy, sinus endoscopy, colonoscopy or laparoscopy, but are limited by an inability to generalize to different domains without retraining. METHODS: To address this generality issue, we propose OneSLAM a monocular SLAM algorithm for surgical endoscopy that works out of the box for several endoscopic domains, including sinus endoscopy, colonoscopy, arthroscopy and laparoscopy. Our pipeline builds upon robust tracking any point (TAP) foundation models to reliably track sparse correspondences across multiple frames and runs local bundle adjustment to jointly optimize camera poses and a sparse 3D reconstruction of the anatomy. RESULTS: We compare the performance of our method against three strong baselines previously proposed for monocular SLAM in endoscopy and general scenes. OneSLAM presents better or comparable performance over existing approaches targeted to that specific data in all four tested domains, generalizing across domains without the need for retraining. CONCLUSION: OneSLAM benefits from the convincing performance of TAP foundation models but generalizes to endoscopic sequences of different anatomies all while demonstrating better or comparable performance over domain-specific SLAM approaches. Future research on global loop closure will investigate how to reliably detect loops in endoscopic scenes to reduce accumulated drift and enhance long-term navigation capabilities.