ArticleInternational journal of computer assisted radiology and surgery2023
Learning feature descriptors for pre- and intra-operative point cloud matching for laparoscopic liver registration.
Article in International journal of computer assisted radiology and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 27 citations in OpenAlex.
- Dual-Expert Landmark Localization in 3D Facial Point Clouds Under Controlled Synthetic Local Surface Loss for Rigid Initialization.Sensors (Basel, Switzerland) · 2026Article
- Correspondence-free local-to-global liver deformation correction via implicit neural representation and biomechanical model.International journal of computer assisted radiology and surgery · 2026Article
- Warm-started reinforcement learning for iterative 3D/2D liver registration.International journal of computer assisted radiology and surgery · 2026Article
- Gia-Net: geometry-informed attention network for 3D point cloud registration in liver surgery.International journal of computer assisted radiology and surgery · 2026Article
- Toward Patient-Specific Partial Point Cloud to Surface Completion for Pre to Intra-operative Registration in Image-Guided Liver Interventions.Medical Image Understanding and Analysis. Medical Image Understanding and Analysis (Conference) · 2026Article
- Resolving the Ambiguity of Complete-to-Partial Point Cloud Registration for Image-Guided Liver Surgery With Patches-to-Partial Matching.IEEE journal of biomedical and health informatics · 2026Article
- Towards a zero-shot low-latency navigation for open surgery augmented reality applications.International journal of computer assisted radiology and surgery · 2025Article
- Non-rigid image-volume registration for human livers in laparoscopic surgery.Quantitative imaging in medicine and surgery · 2025Article
- Boundary Constraint-Free Biomechanical Model-Based Surface Matching for Intraoperative Liver Deformation Correction.IEEE transactions on medical imaging · 2025Article
- Evaluation of Intra-operative Patient-specific Methods for Point Cloud Completion for Minimally Invasive Liver Interventions.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Preoperative and intraoperative laparoscopic liver surface registration using deep graph matching of representative overlapping points.International journal of computer assisted radiology and surgery · 2025Article
- Benchmarking complete-to-partial point cloud registration techniques for laparoscopic surgery.Frontiers in robotics and AI · 2025Article
- Comparative Analysis of Non-Rigid Registration Techniques for Liver Surface Registration.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- A Landmark-Free 3D-2D Rigid Liver Registration via Point Cloud Matching for Laparoscopic Surgery.Healthcare technology lettersArticle
- Assessing Learning-Based Reconstructed Liver Surfaces From Partial Point Clouds for Improving Pre- to Intra-Operative 3D to 3D Registration.Healthcare technology lettersArticle
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
purposeIn laparoscopic liver surgery, preoperative information can be overlaid onto the intra-operative scene by registering a 3D preoperative model to the intra-operative partial surface reconstructed from the laparoscopic video. To assist with this task, we explore the use of learning-based feature descriptors, which, to our best knowledge, have not been explored for use in laparoscopic liver registration. Furthermore, a dataset to train and evaluate the use of learning-based descriptors does not exist.
methodsWe present the LiverMatch dataset consisting of 16 preoperative models and their simulated intra-operative 3D surfaces. We also propose the LiverMatch network designed for this task, which outputs per-point feature descriptors, visibility scores, and matched points.
resultsWe compare the proposed LiverMatch network with a network closest to LiverMatch and a histogram-based 3D descriptor on the testing split of the LiverMatch dataset, which includes two unseen preoperative models and 1400 intra-operative surfaces. Results suggest that our LiverMatch network can predict more accurate and dense matches than the other two methods and can be seamlessly integrated with a RANSAC-ICP-based registration algorithm to achieve an accurate initial alignment.
conclusionThe use of learning-based feature descriptors in laparoscopic liver registration (LLR) is promising, as it can help achieve an accurate initial rigid alignment, which, in turn, serves as an initialization for subsequent non-rigid registration.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.