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Related Experiment Video

Updated: Jun 24, 2026

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy
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Recurrent multi-view 6DoF pose estimation for marker-less surgical tool tracking.

Niklas Agethen1, Janis Rosskamp2, Tom L Koller3,2

  • 1Fraunhofer MEVIS, Max-von-Laue-Str. 2, 28359, Bremen, Germany. niklas.agethen@mevis.fraunhofer.de.

International Journal of Computer Assisted Radiology and Surgery
|June 17, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning approach for marker-less surgical instrument tracking using multiple RGB cameras. The method enhances precision and reliability, especially during instrument occlusion, offering a competitive alternative to traditional marker-based systems.

Keywords:
Marker-less trackingMulti-view object pose estimationRecurrent neural networksSurgical navigation

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Area of Science:

  • Computer Vision
  • Medical Technology
  • Machine Learning

Background:

  • Marker-based tracking in surgical navigation is precise but requires extensive preparation and is susceptible to marker occlusion.
  • Deep learning offers a promising marker-less alternative using RGB videos for surgical instrument tracking.

Purpose of the Study:

  • To apply object pose estimation with a novel deep learning architecture for marker-less surgical instrument tracking.
  • To address challenges of time-consuming preparation and marker occlusion in surgical navigation.

Main Methods:

  • Combined multi-view pose estimation with recurrent neural networks (RNNs) to leverage temporal coherence.
  • Integrated a spatio-temporal feature extractor into an existing pose estimation pipeline for sequence-based feature incorporation.
  • Evaluated performance under conditions of instrument occlusion.

Main Results:

  • Achieved mean tip error below 1.0 mm and angle error below 0.2° on a synthetic dataset with a four-camera setup.
  • Attained an error below 3.0 mm on a real dataset using four cameras.
  • The recurrent approach demonstrated ~3 mm greater precision in tip position prediction during limited instrument visibility compared to non-recurrent methods.

Conclusions:

  • Deep learning-based tracking with multiple cameras shows competitiveness with marker-based systems for surgical instruments.
  • Recurrent temporal information significantly benefits tracking reliability when instruments are occluded.
  • The combination of multi-view processing and recurrent networks enhances the precision and usability of surgical pose estimation.