Reconstructing Sinus Anatomy from Endoscopic Video -- Towards a Radiation-free Approach for Quantitative Longitudinal Assessment
This work addresses the need for radiation-free, quantitative longitudinal assessment of sinus anatomy for surgical outcome analysis, representing a domain-specific incremental advancement.
The paper tackles the problem of reconstructing accurate 3D sinus anatomy from endoscopic video, presenting a patient-specific, learning-based method that produces watertight textured reconstructions enabling clinical measurements in good agreement with CT ground truth.
Reconstructing accurate 3D surface models of sinus anatomy directly from an endoscopic video is a promising avenue for cross-sectional and longitudinal analysis to better understand the relationship between sinus anatomy and surgical outcomes. We present a patient-specific, learning-based method for 3D reconstruction of sinus surface anatomy directly and only from endoscopic videos. We demonstrate the effectiveness and accuracy of our method on in and ex vivo data where we compare to sparse reconstructions from Structure from Motion, dense reconstruction from COLMAP, and ground truth anatomy from CT. Our textured reconstructions are watertight and enable measurement of clinically relevant parameters in good agreement with CT. The source code is available at https://github.com/lppllppl920/DenseReconstruction-Pytorch.