ArticleInternational journal of computer assisted radiology and surgery2022
Using multiple images and contours for deformable 3D-2D registration of a preoperative CT in laparoscopic liver surgery.
Article in International journal of computer assisted radiology and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 14 citations in OpenAlex.
- Correspondence-free local-to-global liver deformation correction via implicit neural representation and biomechanical model.International journal of computer assisted radiology and surgery · 2026Article
- Liver mask-guided SAM-enhanced dual-decoder network for landmark segmentation in AR-guided surgery.International journal of computer assisted radiology and surgery · 2026Article
- Non-rigid image-volume registration for human livers in laparoscopic surgery.Quantitative imaging in medicine and surgery · 2025Article
- MIS-NeRF: neural radiance fields in minimally-invasive surgery.International journal of computer assisted radiology and surgery · 2025Article
- Leveraging augmented reality for dynamic guidance in 3-dimensional laparoscopic and robotic liver surgery: a prospective case series study.Annals of surgical treatment and research · 2025Article
- Boundary Constraint-Free Biomechanical Model-Based Surface Matching for Intraoperative Liver Deformation Correction.IEEE transactions on medical imaging · 2025Article
- Neural patient-specific 3D-2D registration in laparoscopic liver resection.International journal of computer assisted radiology and surgery · 2025Article
- A review of deep learning-based deformable medical image registration.Frontiers in oncology · 2022Review
- A Landmark-Free 3D-2D Rigid Liver Registration via Point Cloud Matching for Laparoscopic Surgery.Healthcare technology lettersArticle
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Authors and funding
6 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeLaparoscopic liver resection is a challenging procedure because of the difficulty to localise inner structures such as tumours and vessels. Augmented reality overcomes this problem by overlaying preoperative 3D models on the laparoscopic views. It requires deformable registration of the preoperative 3D models to the laparoscopic views, which is a challenging task due to the liver flexibility and partial visibility.
methodsWe propose several multi-view registration methods exploiting information from multiple views simultaneously in order to improve registration accuracy. They are designed to work on two scenarios: on rigidly related views and on non-rigidly related views. These methods exploit the liver's anatomical landmarks and texture information available in all the views to constrain registration.
resultsWe evaluated the registration accuracy of our methods quantitatively on synthetic and phantom data, and qualitatively on patient data. We measured 3D target registration errors in mm for the whole liver for the quantitative case, and 2D reprojection errors in pixels for the qualitative case.
conclusionThe proposed rigidly related multi-view methods improve registration accuracy compared to the baseline single-view method. They comply with the 1 cm oncologic resection margin advised for hepatocellular carcinoma interventions, depending on the available registration constraints. The non-rigidly related multi-view method does not provide a noticeable improvement. This means that using multiple views with the rigidity assumption achieves the best overall registration error.
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