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Related Experiment Video

Updated: Jul 9, 2026

Technical Approach for Infrared Tracking for Soft Tissue Navigation with a Holographic Head-Mounted Display and Preclinical Validation
10:25

Technical Approach for Infrared Tracking for Soft Tissue Navigation with a Holographic Head-Mounted Display and Preclinical Validation

Published on: September 2, 2025

A probabilistic framework for tracking deformable soft tissue in minimally invasive surgery.

Peter Mountney1, Benny Lo, Surapa Thiemjarus

  • 1Department of Computing, Imperial College, London SW7 2BZ, UK.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|November 30, 2007
PubMed
Summary

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This study enhances vision-based algorithms for minimally invasive surgery (MIS) by improving soft-tissue deformation tracking. A new framework and Bayesian fusion method increase accuracy for surgical guidance and robotic navigation.

Area of Science:

  • Computer Vision
  • Surgical Robotics
  • Medical Imaging

Background:

  • Vision-based algorithms are crucial for 3D tissue deformation recovery in minimally invasive surgery (MIS).
  • Existing computer vision feature descriptors face challenges with free-form tissue deformation and visual variability in surgical scenes.
  • Accurate deformation tracking is essential for intra-operative surgical guidance and robotic navigation.

Purpose of the Study:

  • To evaluate the performance of current state-of-the-art feature descriptors for soft-tissue deformation tracking in MIS.
  • To introduce a novel probabilistic framework for selecting discriminative feature descriptors.
  • To enhance the accuracy and temporal stability of soft-tissue deformation tracking using a Bayesian fusion method.

Main Methods:

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Last Updated: Jul 9, 2026

Technical Approach for Infrared Tracking for Soft Tissue Navigation with a Holographic Head-Mounted Display and Preclinical Validation
10:25

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Published on: September 2, 2025

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics
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Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics

Published on: April 16, 2017

  • Evaluation of various computer vision feature descriptors on their suitability for surgical deformation tracking.
  • Development of a probabilistic framework to identify the most discriminative feature descriptors.
  • Implementation of a Bayesian fusion technique to combine information from selected descriptors for improved tracking.

    • Main Results:

    • Identified performance limitations of existing feature descriptors in the context of MIS.
    • Demonstrated the effectiveness of the proposed probabilistic framework in selecting optimal descriptors.
    • Showcased significant improvements in accuracy and temporal persistence of soft-tissue deformation tracking using the Bayesian fusion method.
    • Validated the approach using both simulated and in vivo robotic-assisted MIS data.

    Conclusions:

    • The proposed probabilistic framework and Bayesian fusion method offer a robust solution for soft-tissue deformation tracking in MIS.
    • This advancement has the potential to improve intra-operative surgical guidance and robotic navigation.
    • The findings pave the way for more reliable vision-based systems in robotic surgery.