A System for Visualizing and Assessing Electromagnetic Tracking Error during Computer-‐assisted Surgery
Bibliographic record
Abstract
PURPOSE: Electromagnetic tracking is used in image-‐guided interventions to monitor the position of surgical equipment. However, it is prone to error. During navigation procedures, the measurement and visualization of error should take place to ensure precision and accuracy. Our goal was to extend open-‐source software such that no programming from the user is needed for error monitoring. METHODS: The electromagnetic tracking error was defined as the difference in position readings of a surgical stylus that was optically and electromagnetically tracked. The optical tracking reading was considered ground truth as it is unaffected by metal objects. The stylus was moved freehandedly within a region of interest to quickly sample electromagnetic tracking error, including error caused by field-‐distortion inducing ferromagnetic materials and random noise. All tracked devices were used in a plug-‐and-‐play manner. Measurements were visualized in real-‐time in 3D Slicer, an open-‐ source platform for medical image computing (www.slicer.org). RESULTS: To determine the reproducibility of the data collected by our system, the tracking error was measured in regions of interest representing a surgeon’s workspace. Tests were done with and without metal objects placed in the workspace. A quick freehand sampling procedure was sufficient to detect error. Freehand measurements within a controlled environment reported distortion values of 1.16 mm (STD 0.71 mm). Measurements taken with a metal rod and parts from a surgical retractor kit were 4.27 mm (STD 2.92 mm) and 8.26 mm (STD 2.08 mm), respectively. CONCLUSION: Our system shows promise for the real-‐time visualization of electromagnetic tracking error during computer-‐assisted surgical procedures
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".