Dynamic uncertainty-level assessment framework for real-time needle tracking in CT-guided surgical environments.
Authors
Affiliations (4)
Affiliations (4)
- Otto von Guericke University Magdeburg, Faculty of Computer Science, Chair of Virtual and Augmented Reality, Universitaetsplatz 2, 39106, Magdeburg, Germany. [email protected].
- Research Campus STIMULATE, Otto-Hahn-Str. 2, 39106, Magdeburg, Germany. [email protected].
- Otto von Guericke University Magdeburg, Faculty of Computer Science, Chair of Virtual and Augmented Reality, Universitaetsplatz 2, 39106, Magdeburg, Germany.
- Research Campus STIMULATE, Otto-Hahn-Str. 2, 39106, Magdeburg, Germany.
Abstract
Accurate needle tracking is critical for the success of computed tomography (CT)-guided interventions, where even minor deviations may compromise procedural safety and clinical outcomes. However, existing image-guided tracking systems typically lack mechanisms to quantify and communicate the reliability of their predictions in real time, leaving clinicians to act on guidance of uncertain trustworthiness. We propose a Dynamic Uncertainty Level Assessment Framework that provides a quantitative, real-time estimate of tracking reliability by linearly linking a predicted uncertainty score to spatial tracking error. The framework consists of three different approaches: (1) a classic method based on dynamically weighted, interpretable reliability metrics; (2) a lightweight convolutional neural network (CNN) that predicts uncertainty directly from multi-view image data; and (3) a hybrid CNN that adaptively optimizes metric weights while preserving interpretability. The uncertainty level is defined on a fixed scale, with <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mn>0</mn> <mo>%</mo></mrow> </math> corresponding to ideal tracking (error of 0 mm) and <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mn>100</mn> <mo>%</mo></mrow> </math> to a tracking error of 10 mm. Experimental validation on clinical and clinically realistic laboratory datasets of <math xmlns="http://www.w3.org/1998/Math/MathML"><mo>∼</mo></math> 30,000 frames demonstrates a strong positive correlation between uncertainty and error (Pearson <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>r</mi> <mo>></mo> <mn>0.82</mn></mrow> </math> ) and achieves a stable tracking error estimation (error difference <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mo><</mo> <mn>0.6</mn></mrow> </math> mm) with real-time performance (5 ms per frame). By enabling an intuitive uncertainty-to-error mapping, the proposed framework supports more informed intra-procedural decision-making, enhances operator trust in guidance data, and establishes a practical basis for integration into uncertainty-aware CT-guided intervention systems.