Accuracy and reproducibility of automatic versus manual registration using a cone-beam CT image guidance system.
Bibliographic record
Abstract
INTRODUCTION: Intraoperative imaging reveals morphologic changes and resolves anatomic uncertainties during surgery. The automatic registration (AR) approach provides registered intraoperative images for real-time tracking within seconds of acquisition. PURPOSE: (1) To design an AR device for clinical use integrated with cone-beam computed tomography, (2) to compare the accuracy and reproducibility of manual and automatic registration, and (3) to evaluate the robustness of the AR system. METHODS: An AR device consisting of an acrylic face shield with fiducials mounted on an adjustable arm was designed. Eight surface and five internal divot markers were placed with bony fixation to a cadaveric head. Internal markers were localized on the image representing the "true" location. This was compared to the positions localized using a navigational system when both manual registration and AR were applied. A series of surgical tasks and variation of the AR device height above the surgical field was performed, and target registration error (TRE) was measured. RESULTS: The mean fiducial registration error (FRE) for manual and automatic registration was 0.72 mm ± 0.03 and 0.41 mm ± 0.01, respectively. The mean TRE for manual and automatic registration was 0.89 mm ± 0.26 and 0.91 mm ± 0.25, respectively. CONCLUSIONS: AR offers a more accurate and reproducible FRE and a TRE equally comparable to that of manual registration. This system also demonstrates robustness with comparable accuracy and reproducibility throughout different surgical tasks and variation of AR device height up to 9 cm above the surgical field. This system is currently being translated into clinical trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".