mlsanityData to 7 Oct 2026

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

arXiv 2403.16736 ↗First posted 25 Mar 2024cs.CVcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 25 of its 54 references that are in the map. First posted on arXiv 25 Mar 2024.

Builds on

25 of 54 references.

References · 25

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  2. 2018OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,161
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  4. 2023Neuralangelo: High-Fidelity Neural Surface Reconstructioncited by 464
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  9. 2020Surgical Data Science -- from Concepts toward Clinical Translationcited by 105
  10. 2021OperA: Attention-Regularized Transformers for Surgical Phase Recognitioncited by 60
  11. 2018MVOR: A Multi-view RGB-D Operating Room Dataset for 2D and 3D Human Pose Estimationcited by 49
  12. 2023Advancing Surgical VQA with Scene Graph Knowledgecited by 41
13 more
  1. 2021You Only Learn Once: Universal Anatomical Landmark Detectioncited by 29
  2. 2021SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot Learningcited by 28
  3. 2023ResFields: Residual Neural Fields for Spatiotemporal Signalscited by 26
  4. 2023LABRAD-OR: Lightweight Memory Scene Graphs for Accurate Bimodal Reasoning in Dynamic Operating Roomscited by 21
  5. 2023Dynamic Scene Graph Representation for Surgical Videocited by 21
  6. 2017A Multi-view RGB-D Approach for Human Pose Estimation in Operating Roomscited by 17
  7. 2021Robotic Surgery With Lean Reinforcement Learningcited by 11
  8. 2023Automatic registration with continuous pose updates for marker-less surgical navigation in spine surgerycited by 11
  9. 2022Dynamic Depth-Supervised NeRF for Multi-View RGB-D Operating Room Imagescited by 7
  10. 2021Multimodal Semantic Scene Graphs for Holistic Modeling of Surgical Procedurescited by 6
  11. 2022HMD-EgoPose: Head-Mounted Display-Based Egocentric Marker-Less Tool and Hand Pose Estimation for Augmented Surgical Guidancecited by 5
  12. 2016Articulated Clinician Detection Using 3D Pictorial Structures on RGB-D Datacited by 3
  13. 2024Dense 3D Reconstruction Through Lidar: A Comparative Study on Ex-vivo Porcine Tissuecited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 6; 2026: 1.

9 papers.