%0 Journal Article
%T An endoscopic chisel: intraoperative imaging carves 3D anatomical models.
%A Mangulabnan JE
%A Soberanis-Mukul RD
%A Teufel T
%A Sahu M
%A Porras JL
%A Vedula SS
%A Ishii M
%A Hager G
%A Taylor RH
%A Unberath M
%J Int J Comput Assist Radiol Surg
%V 19
%N 7
%D 2024 Jul 16
%M 38753135
%F 3.421
%R 10.1007/s11548-024-03151-w
%X <B>OBJECTIVE: </B>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.<BR><B>METHODS: </B>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.<BR><B>CONCLUSIONS: </B>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.