Neural fields for 3D tracking of anatomy and surgical instruments in monocular laparoscopic video clips

Abstract

Laparoscopic video tracking primarily focuses on two target types: surgical instruments and anatomy. The former could be used for skill assessment, while the latter is necessary for the projection of virtual overlays. Where instrument and anatomy tracking have often been considered two separate problems, in this article, a method is proposed for joint tracking of all structures simultaneously. Based on a single 2D monocular video clip, a neural field is trained to represent a continuous spatiotemporal scene, used to create 3D tracks of all surfaces visible in at least one frame. Due to the small size of instruments, they generally cover a small part of the image only, resulting in decreased tracking accuracy. Therefore, enhanced class weighting is proposed to improve the instrument tracks. The authors evaluate tracking on video clips from laparoscopic cholecystectomies, where they find mean tracking accuracies of 92.4% for anatomical structures and 87.4% for instruments. Additionally, the quality of depth maps obtained from the method's scene reconstructions is assessed. It is shown that these pseudo-depths have comparable quality to a state-of-the-art pre-trained depth estimator. On laparoscopic videos in the SCARED dataset, the method predicts depth with an MAE of 2.9 mm and a relative error of 9.2%. These results show the feasibility of using neural fields for monocular 3D reconstruction of laparoscopic scenes. Code is available via GitHub: https://github.com/Beerend/Surgical-OmniMotion.