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.

TAToo: vision-based joint tracking of anatomy and tool for skull-base surgery.

Purpose: Tracking the 3D motion of the surgical tool and the patient anatomy is a fundamental requirement for computer-assisted skull-base surgery. The estimated motion can be used both for intra-operative guidance and for downstream skill analysis. Recovering such motion solely from surgical videos is desirable, as it is compliant with current clinical workflows and instrumentation.

Twin-S: a digital twin for skull base surgery.

Purpose: Digital twins are virtual replicas of real-world objects and processes, and they have potential applications in the field of surgical procedures, such as enhancing situational awareness. We introduce Twin-S, a digital twin framework designed specifically for skull base surgeries.

Methods: Twin-S is a novel framework that combines high-precision optical tracking and real-time simulation, making it possible to integrate it into image-guided interventions.