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Purpose <p>Neurosurgeons 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.</p> Methods <p>We 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.</p> Results <p>Six 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 <i>surgeon-centric</i> 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.</p> Conclusions <p>We 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.</p>

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Optimizing registration uncertainty visualization to support intraoperative decision-making during brain tumor resection

  • 期刊论文

  • 发表日期：

    2025年5月13日

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  • M. Geshvadi,
  • R. Dorent,
  • C. Galvin,
  • L. Rigolo,
  • N. Haouchine,
  • T. Kapur,
  • S. Pieper,
  • M. Vangel,
  • W. M. Wells,
  • A. J. Golby,
  • D. Haehn,
  • S. Frisken

摘要

Purpose

Neurosurgeons 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.

Methods

We 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.

Results

Six 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.

Conclusions

We 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.