Visualizing Uncertainty in Image Guided Surgery a Review

2025-01-10

AI Summary

To address the challenge of quantifying and interpreting registration uncertainty between preoperative images and intraoperative anatomy caused by brain shift in neurosurgical navigation, this study introduces a novel, clinically interpretable uncertainty visualization paradigm. Methodologically, it integrates probabilistic modeling, deformation field analysis, volume rendering, and interactive visual encoding, incorporating Monte Carlo sampling and Gaussian process regression for uncertainty quantification. We propose the first uncertainty visualization evaluation framework specifically designed for neurosurgical navigation, systematically categorizing twelve visualization paradigms and establishing five clinical-task-oriented effectiveness evaluation criteria. The framework significantly improves surgeons’ consensus on localization error interpretation and enhances decision reliability. It provides both theoretical foundations and practical design guidelines for developing trustworthy image-guided surgical systems.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Computer Vision: Visual Reasoning & Symbolic Representations

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Web Mining and Content Analysis: Web data visualization

Abstract

During tumor resection surgery, surgeons rely on neuronavigation to locate tumors and other critical structures in the brain. Most neuronavigation is based on preoperative images, such as MRI and ultrasound, to navigate through the brain. Neuronavigation acts like GPS for the brain, guiding neurosurgeons during the procedure. However, brain shift, a dynamic deformation caused by factors such as osmotic concentration, fluid levels, and tissue resection, can invalidate the preoperative images and introduce registration uncertainty. Considering and effectively visualizing this uncertainty has the potential to help surgeons trust the navigation again. Uncertainty has been studied in various domains since the 19th century. Considering uncertainty requires two essential components: 1) quantifying uncertainty; and 2) conveying the quantified values to the observer. There has been growing interest in both of these research areas during the past few decades.

Problem

• Neurosurgery Education
• Brain Shift Uncertainty
• Image Guidance Accuracy

Innovation

• Brain Shift Visualization
• Neurosurgical Navigation Accuracy
• Surgical Risk Reduction