Measurement of electromagnetic tracking error in a navigated breast surgery setup
Bibliographic record
Abstract
PURPOSE: The measurement of tracking error is crucial to ensure the safety and feasibility of electromagnetically tracked, image-guided procedures. Measurement should occur in a clinical environment because electromagnetic field distortion depends on positioning relative to the field generator and metal objects. However, we could not find an accessible and open-source system for calibration, error measurement, and visualization. We developed such a system and tested it in a navigated breast surgery setup. METHODS: A pointer tool was designed for concurrent electromagnetic and optical tracking. Software modules were developed for automatic calibration of the measurement system, real-time error visualization, and analysis. The system was taken to an operating room to test for field distortion in a navigated breast surgery setup. Positional and rotational electromagnetic tracking errors were then calculated using optical tracking as a ground truth. RESULTS: Our system is quick to set up and can be rapidly deployed. The process from calibration to visualization also only takes a few minutes. Field distortion was measured in the presence of various surgical equipment. Positional and rotational error in a clean field was approximately 0.90 mm and 0.31°. The presence of a surgical table, an electrosurgical cautery, and anesthesia machine increased the error by up to a few tenths of a millimeter and tenth of a degree. CONCLUSION: In a navigated breast surgery setup, measurement and visualization of tracking error defines a safe working area in the presence of surgical equipment. Our system is available as an extension for the open-source 3D Slicer platform.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".