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Related Experiment Video

Updated: Sep 22, 2025

Three-dimensional Location Approach with Silk Thread Guided Laparoscopic Segmentectomy for Liver Tumor
06:39

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Automatic preoperative 3d model registration in laparoscopic liver resection.

M Labrunie1,2, M Ribeiro3, F Mourthadhoi4

  • 1Université Clermont Auvergne, Clermont Auvergne INP, CHU Clermont-Ferrand, CNRS, Institut Pascal, 63000, Clermont-Ferrand, France. mathieu.labrunie@etu.uca.fr.

International Journal of Computer Assisted Radiology and Surgery
|May 22, 2022
PubMed
Summary

This study introduces automated methods for augmented reality (AR) in laparoscopic liver resection, reducing surgeon workload by automatically identifying anatomical landmarks and improving 3D model registration accuracy.

Keywords:
3D-2D registrationCurvilinear landmark detectionLaparoscopic liver resectionModel initialisationMonocular vision

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Area of Science:

  • Medical Imaging
  • Computer-Assisted Surgery
  • Augmented Reality

Background:

  • Augmented Reality (AR) systems in laparoscopic liver resection rely on accurate registration of preoperative 3D models with intraoperative laparoscopic images.
  • Current AR systems require surgeons to manually identify anatomical landmarks and provide initial registration, increasing procedural burden and potential for error.

Purpose of the Study:

  • To develop and validate automated methods for identifying anatomical landmarks and performing initial registration in AR-assisted laparoscopic liver resection.
  • To reduce surgeon attention required for landmark annotation and registration, thereby streamlining the surgical procedure.

Main Methods:

  • A U-Net model was trained on a dataset of 1415 labeled images from 68 procedures to detect anatomical landmarks (lower ridge, falciform ligament) and the liver silhouette.
  • A novel coarse-to-fine pose estimation method with visibility reasoning was developed for automatic initial registration.
  • The liver ridge was divided into six anatomical sub-parts to enhance annotation and registration accuracy.

Main Results:

  • The automated method achieved silhouette detection comparable to experienced surgeons.
  • Landmark detection showed higher errors due to under-detection but enabled successful registration initialization.
  • Tumor target registration errors for automated initialization were 22.4, 14.8, and 7.2 mm in three clinical cases, outperforming manual initialization errors (30.5, 15.1, and 16.3 mm).

Conclusions:

  • The proposed automated methods show promising results for AR-assisted laparoscopic liver resection.
  • The approach offers a viable solution to reduce surgeon workload and potentially improve registration accuracy in complex surgical navigation.