Creating a Digital Twin of Spinal Surgery: A Proof of Concept

Jonas Hein, Frédéric Giraud, Lilian Calvet, Alexander Schwarz, Nicola Alessandro Cavalcanti, Sergey Prokudin, Mazda Farshad, Siyu Tang, Marc Pollefeys, Fabio Carrillo, Philipp Fürnstahl; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 2355-2364

Surgery digitalization is the process of creating a virtual replica of real-world surgery also referred to as a surgical digital twin (SDT). It has significant applications in various fields such as education and training surgical planning and automation of surgical tasks. In addition SDTs are an ideal foundation for machine learning methods enabling the automatic generation of training data. In this paper we present a proof of concept (PoC) for surgery digitalization that is applied to an ex-vivo spinal surgery. The proposed digitalization focuses on the acquisition and modelling of the geometry and appearance of the entire surgical scene. We employ five RGB-D cameras for dynamic 3D reconstruction of the surgeon a high-end camera for 3D reconstruction of the anatomy an infrared stereo camera for surgical instrument tracking and a laser scanner for 3D reconstruction of the operating room and data fusion. We justify the proposed methodology discuss the challenges faced and further extensions of our prototype. While our PoC partially relies on manual data curation its high quality and great potential motivate the development of automated methods for the creation of SDTs.