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Acquiring submillimeter-accurate multi-task vision datasets for computer-assisted orthopedic surgery.

Emma Most1,2, Jonas Hein3,4, Frédéric Giraud3

  • 1Research in Orthopedic Computer Science, University Hospital Balgrist, University of Zurich, Zurich, Switzerland. emmost@ethz.ch.

International Journal of Computer Assisted Radiology and Surgery
|May 14, 2025
PubMed
Summary

This study presents a novel framework for generating accurate 3D ground truth datasets for open orthopedic surgery. The method achieves submillimeter accuracy, enabling advancements in computer vision for surgical navigation and digitization.

Keywords:
3D reconstructionFeature matchingOpen orthopedic surgery datasetSurgery digitizationSurgical navigation

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Area of Science:

  • Computer Vision
  • Medical Imaging
  • Surgical Technology

Background:

  • Computer vision advancements, especially in 3D reconstruction and feature matching, are crucial for marker-less surgical navigation and digitization.
  • The development of these technologies is limited by the scarcity of suitable datasets with 3D ground truth.
  • Generating realistic and accurate ex vivo datasets is essential for advancing surgical applications.

Purpose of the Study:

  • To explore and develop an approach for generating realistic and accurate ex vivo datasets for 3D reconstruction and feature matching in open orthopedic surgery.
  • To address the lack of suitable 3D ground truth datasets hindering computer vision applications in surgery.

Main Methods:

  • Proposed a framework involving three core steps: 3D scanning, viewpoint calibration for high-resolution RGB images, and optical scene registration.
  • Compared different methods for each step of the framework.
  • Evaluated the framework on an ex vivo scoliosis surgery dataset using a pig spine under operating room conditions.

Main Results:

  • Achieved a mean 3D Euclidean error of 0.35 mm with respect to the 3D ground truth.
  • Demonstrated submillimeter-accurate 3D ground truths.
  • Acquired surgical images with a spatial resolution of 0.1 mm.

Conclusions:

  • The proposed framework successfully generates high-accuracy 3D ground truth data for surgical applications.
  • This approach facilitates the acquisition of future surgical datasets for high-precision computer vision tasks.
  • Enables advancements in marker-less surgical navigation and digitization through improved dataset availability.