Waleed M. Abuzeid | Searcharxiv

arXiv2026

Virtual Intraoperative CT (viCT): Sequential Anatomic Updates for Modeling Tissue Resection Throughout Endoscopic Sinus Surgery

Purpose: Incomplete dissection is a common cause of persistent disease and revision endoscopic sinus surgery (ESS) in chronic rhinosinusitis. Current image-guided surgery systems typically reference static preoperative CT (pCT), and do not model evolving resection boundaries. We present Virtual Intraoperative CT (viCT), a method for sequentially updating pCT throughout ESS using intraoperative 3D reconstructions from monocular endoscopic video to enable visualization of evolving anatomy in CT format. Methods: Monocular endoscopic video is processed using a depth-supervised NeRF framework with virtual stereo synthesis to generate metrically scaled 3D reconstructions at multiple surgical intervals. Reconstructions undergo rigid, landmark-based registration in 3D Slicer guided by anatomical correspondences, and are then voxelized into the pCT grid. viCT volumes were generated using a ray-based occupancy comparison between pCT and reconstruction to delete outdated voxels and remap preserved anatomy and updated boundaries. Performance is evaluated in a cadaveric feasibility study of four specimens across four ESS stages using volumetric overlap (DSC, Jaccard) and surface metrics (HD95, Chamfer, MSD, RMSD), and qualitative comparisons to ground-truth CT. Results: viCT updates show agreement with ground-truth anatomy across surgical stages, with submillimeter mean surface errors. Dice Similarity Coefficient (DSC) = 0.88 +/- 0.05 and Jaccard Index = 0.79 +/- 0.07, and Hausdorff Distance 95% (HD95) = 0.69 +/- 0.28 mm, Chamfer Distance = 0.09 +/- 0.05 mm, Mean Surface Distance (MSD) = 0.11 +/- 0.05 mm, and Root Mean Square Distance (RMSD) = 0.32 +/- 0.10 mm. Conclusion: viCT enables CT-format anatomic updating in an ESS setting without ancillary hardware. Future work will focus on fully automating registration, validation in live cases, and optimizing runtime for real-time deployment.

cs.CV↗

arXiv2025

EndoPerfect: High-Accuracy Monocular Depth Estimation and 3D Reconstruction for Endoscopic Surgery via NeRF-Stereo Fusion

In endoscopic sinus surgery (ESS), intraoperative CT (iCT) offers valuable intraoperative assessment but is constrained by slow deployment and radiation exposure, limiting its clinical utility. Endoscope-based monocular 3D reconstruction is a promising alternative; however, existing techniques often struggle to achieve the submillimeter precision required for dense reconstruction. In this work, we propose an iterative online learning approach that leverages Neural Radiance Fields (NeRF) as an intermediate representation, enabling monocular depth estimation and 3D reconstruction without relying on prior medical data. Our method attains a point-to-point accuracy below 0.5 mm, with a demonstrated theoretical depth accuracy of 0.125 $\pm$ 0.443 mm. We validate our approach across synthetic, phantom, and real endoscopic scenarios, confirming its accuracy and reliability. These results underscore the potential of our pipeline as an iCT alternative, meeting the demanding submillimeter accuracy standards required in ESS.

eess.IV↗