  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.
