Dynamic uncertainty-level assessment framework for real-time needle tracking in CT-guided surgical environments

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 $0\%$ corresponding to ideal tracking (error of 0 mm) and $100\%$ to a tracking error of 10 mm.

Experimental validation on clinical and clinically realistic laboratory datasets of $\sim$ 30,000 frames demonstrates a strong positive correlation between uncertainty and error (Pearson $r>0.82$ ) and achieves a stable tracking error estimation (error difference $<0.6$  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.