ArticleProceedings of SPIE--the International Society for Optical Engineering2025
Novel view synthesis using neural radiance fields for laparoscopic surgery navigation.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- High-speed optical tracking and augmented reality platform for image-guided interventions.Journal of medical imaging (Bellingham, Wash.) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Laparoscopic surgery is a widely used minimally invasive technique to treat a variety of pathologies within the abdominopelvic region. Compared to conventional open surgery, laparoscopic surgery reduces iatrogenic trauma and hospital stay length. Nevertheless, surgeons have a limited field of view during operation because surgical instruments are inserted through the patient's body via small incisions. To improve surgeon's perception, surgical navigation techniques are implemented to improve visualization. A number of surgical navigation systems being developed are integrated with computer algorithms to enhance their efficiency. In this work, we generate novel views, depths, and camera poses of corresponding laparoscopic images to augment our dataset, which can then be used to train and evaluate learning-based computer vision algorithms such as simultaneous localization and mapping (SLAM) and visual odometry. Our existing dataset was generated from porcine tissue images using a laparoscope and an optical tracking system to capture camera poses. We performed tissue surface reconstruction of the dataset via the NeRF-SLAM algorithm. Camera poses were transformed to acquire novel views of RGB laparoscopic images and their corresponding depth images. The results show that the simulated RGB images have an average peak signal-to-noise ratios (PSNR) between 29.17 - 29.79 dB, structural similarity index measure (SSIM) value between 0.6061 - 0.6174, and learned perceptual image patch similarity (LPIPS) value between 0.568 - 0.592 compared to the corresponding input images. This work provides a foundation for the generation of synthetic datasets, which could be useful for learning-based autonomous surgical navigation systems.
Indexed as
Identifiers
What 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.