ArticleInternational journal of computer assisted radiology and surgery2025
Optimizing registration uncertainty visualization to support intraoperative decision-making during brain tumor resection.
Article in International journal of computer assisted radiology and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- A 3-D Cross-Modal Keypoint Descriptor for MR-US Matching and Registration.IEEE transactions on medical imaging · 2026Article
- Mini-review on human-centered assurance in robot-assisted orthopedics and neurosurgery.Frontiers in robotics and AI · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
purposeNeurosurgeons need to precisely localize and resect tumors without damaging critical brain tissue. However, deformation of the brain (i.e., 'brain shift') and other factors introduce uncertainty during image-guided surgery. We present a new visualization software that supports qualitative and quantitative exploration of the effectiveness of a broad range of methods for communicating uncertainty. We expect that the ability to visualize uncertainty during surgery will help surgeons better understand uncertainty in neuronavigation and make more informed decisions.
methodsWe developed UVisExplore, a software module for exploring various visualization techniques for understanding the spatial distribution of uncertainty in image registration. UVisExplore incorporates multiple classic uncertainty visualization techniques and introduces two novel paradigms appropriate for surgical environments. We also introduce a novel game-based approach to evaluate visualization effectiveness before surgery. The game scenario emulates the cognitive decision-making process during tumor resection allowing quantitative evaluation of visualization effectiveness in a non-threatening environment while training neurosurgeons to better understand uncertainty.
resultsSix clinicians and three computer scientists participated in a study using our game. Participants explored different uncertainty visualization techniques in a tumor resection task and provided feedback. Surgeon-participants preferred surgeon-centric approaches, which emphasize uncertainty near the surgical probe. They also preferred explicit numerical measures of uncertainty displayed in millimeters. The game provided valuable insights into uncertainty visualization preferences and interpretation.
conclusionsWe provide an open-source 3D Slicer module for visualizing registration uncertainty and a game that allows users to explore uncertainty visualization for tumor resection surgery. UVisExplore provides a platform for exploring and comparing various uncertainty visualization techniques while simulating the decision-making process during surgery. The visualization module and the game proved to be a valuable communication tool and helped narrow the field of candidate visualizations that we plan to test during surgical procedures in the next phase of our research.
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.