Dynamic uncertainty-level assessment framework for real-time needle tracking in CT-guided surgical environments
- 1Otto von Guericke University Magdeburg, Faculty of Computer Science, Chair of Virtual and Augmented Reality, Universitaetsplatz 2, 39106 Magdeburg, Germany
- 2Research Campus STIMULATE, Otto-Hahn-Str. 2, 39106 Magdeburg, Germany
Abstract
Purpose:: 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.
Methods:: 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%0% corresponding to ideal tracking (error of 0 mm) and 100%100% to a tracking error of 10 mm.
Results:: Experimental validation on clinical and clinically realistic laboratory datasets of ∼ ∼30,000 frames demonstrates a strong positive correlation between uncertainty and error (Pearson r > 0.82r>0.82) and achieves a stable tracking error estimation (error difference <0.6<0.6 mm) with real-time performance (5 ms per frame).
Conclusion:: 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.
Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s11548-026-03725-w.
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BibTeX
@article{steiger2026dynamic,
title = {{Dynamic uncertainty-level assessment framework for real-time needle tracking in CT-guided surgical environments}},
author = {Max Steiger and Mohammad Rezapourian and Marko Rak and Christian Hansen},
journal = {International Journal of Computer Assisted Radiology and Surgery},
year = {2026},
month = jun,
doi = {10.1007/s11548-026-03725-w},
url = {https://doi.org/10.1007/s11548-026-03725-w},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Dynamic uncertainty-level assessment framework for real-time needle tracking in CT-guided surgical environments
AU - Max Steiger
AU - Mohammad Rezapourian
AU - Marko Rak
AU - Christian Hansen
PY - 2026
DA - 2026/06/05
JO - International Journal of Computer Assisted Radiology and Surgery
DO - 10.1007/s11548-026-03725-w
UR - https://doi.org/10.1007/s11548-026-03725-w
AB - Purpose:: 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. Methods:: 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%0% corresponding to ideal tracking (error of 0 mm) and 100%100% to a tracking error of 10 mm. Results:: Experimental validation on clinical and clinically realistic laboratory datasets of ∼ ∼30,000 frames demonstrates a strong positive correlation between uncertainty and error (Pearson r > 0.82r>0.82) and achieves a stable tracking error estimation (error difference <0.6<0.6 mm) with real-time performance (5 ms per frame). Conclusion:: 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. Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s11548-026-03725-w.
SN - 1861-6410
SN - 1861-6429
ER -