ArticleInternational journal of computer assisted radiology and surgery2019
A markerless automatic deformable registration framework for augmented reality navigation of laparoscopy partial nephrectomy.
Article in International journal of computer assisted radiology and surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Augmented Reality Implementation in Minimally Invasive Surgery for Future Application in Pulmonary Surgery: A Systematic Review.Surgical innovation · 2024Pooled it
- Augmented Reality (AR) for Surgical Robotic and Autonomous Systems: State of the Art, Challenges, and Solutions.Sensors (Basel, Switzerland) · 2023Pooled it
- GDLS-D: two-stage medical surface reconstruction via geometric diffusion least squares and iterative deformation optimization.Medical & biological engineering & computing · 2026Article
- St-Swin TransNet: a spatiotemporal swin transformer-based network for self-supervised depth estimation in stereoscopic surgical videos.International journal of computer assisted radiology and surgery · 2026Article
- Monocular Surface Reconstructions in Transcanal Endoscopic Ear Surgery: A Phantom Feasibility Study.Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology · 2026Article
- Artificial intelligence in urology training Enhancing annotation, feedback, and evaluation in robotic, laparoscopic, and endoscopic surgery.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2026Review
- Gia-Net: geometry-informed attention network for 3D point cloud registration in liver surgery.International journal of computer assisted radiology and surgery · 2026Article
- Landmark-free automatic digital twin registration in robot-assisted partial nephrectomy using a generic end-to-end model.International journal of computer assisted radiology and surgery · 2025Article
- A novel 3D image registration technique for augmented reality vision in minimally invasive thoracoscopic pulmonary segmentectomy.International journal of computer assisted radiology and surgery · 2025Article
- Applications of mixed reality with medical imaging for training and clinical practice.Journal of medical imaging (Bellingham, Wash.) · 2024Review
- The Role of Artificial Intelligence on Tumor Boards: Perspectives from Surgeons, Medical Oncologists and Radiation Oncologists.Current oncology (Toronto, Ont.) · 2024Review
- Current Application of Navigation Systems in Robotic-Assisted and Laparoscopic Partial Nephrectomy: Focus on the Improvement of Surgical Performance and Outcomes.Annals of surgical oncology · 2024Review
- ASO Author Reflections: Clinical Value of Navigation Systems for RAPN and LPN Procedures.Annals of surgical oncology · 2024Article
- Modern Image-Guided Surgery: A Narrative Review of Medical Image Processing and Visualization.Sensors (Basel, Switzerland) · 2023Review
- On-Device Execution of Deep Learning Models on HoloLens2 for Real-Time Augmented Reality Medical Applications.Sensors (Basel, Switzerland) · 2023Article
- Artificial Intelligence in Surgical Training for Kidney Cancer: A Systematic Review of the Literature.Diagnostics (Basel, Switzerland) · 2023Review
- Image-to-Patient Registration in Computer-Assisted Surgery of Head and Neck: State-of-the-Art, Perspectives, and Challenges.Journal of clinical medicine · 2023Review
- Intraoperative laparoscopic liver surface registration with preoperative CT using mixing features and overlapping region masks.International journal of computer assisted radiology and surgery · 2023Article
- Renal surface reconstruction and segmentation for image-guided surgical navigation of laparoscopic partial nephrectomy.Biomedical engineering letters · 2023Article
- A High-Speed Hyperspectral Laparoscopic Imaging System.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Purpose Video see-through augmented reality (VST-AR) navigation for laparoscopic partial nephrectomy (LPN) can enhance intraoperative perception of surgeons by visualizing surgical targets and critical structures of the kidney tissue. Image registration is the main challenge in the procedure. Existing registration methods in laparoscopic navigation systems suffer from limitations such as manual alignment, invasive external marker fixation, relying on external tracking devices with bulky tracking sensors and lack of deformation compensation. To address these issues, we present a markerless automatic deformable registration framework for LPN VST-AR navigation.
methodDense stereo matching and 3D reconstruction, automatic segmentation and surface stitching are combined to obtain a larger dense intraoperative point cloud of the renal surface. A coarse-to-fine deformable registration is performed to achieve a precise automatic registration between the intraoperative point cloud and the preoperative model using the iterative closest point algorithm followed by the coherent point drift algorithm. Kidney phantom experiments and in vivo experiments were performed to evaluate the accuracy and effectiveness of our approach.
resultsThe average segmentation accuracy rate of the automatic segmentation was 94.9%. The mean target registration error of the phantom experiments was found to be 1.28 ± 0.68 mm (root mean square error). In vivo experiments showed that tumor location was identified successfully by superimposing the tumor model on the laparoscopic view.
conclusionExperimental results have demonstrated that the proposed framework could accurately overlay comprehensive preoperative models on deformable soft organs automatically in a manner of VST-AR without using extra intraoperative imaging modalities and external tracking devices, as well as its potential clinical use.
Indexed as
Identifiers
31016562What OpenQuestion holds
Registered trials
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.