Accuracy and precision of image-based strain measurement using embedded radiopaque markers
Bibliographic record
Abstract
The purpose of this work was to assess the resolution to which micro-CT and intra-operative CT systems can quantify distances between radiopaque fiducial markers. Twenty-two markers were cast in a silicone phantom, then imaged at ten random rotations and translations within the field of view of a micro-CT and an intraoperative CT. A bounding box method and a mask-based weighted binary method were used to calculate the location of all markers in an image, then the Euclidian distance between neighbouring marker coordinates was calculated. The standard deviation in the inter-marker distance measurements from each of the marker position methods across the ten repeated trials was calculated for each marker identification method to provide a measure of the precision of the strain measurement with each scanner. The imaging systems measured 3D distances between markers to within 0.007 mm and 0.028 mm in the micro-CT and intra-operative CT, respectively, using the bounding box method, and to within 0.011 mm and 0.040 mm in the micro-CT and intra-operative systems, respectively, using the weighted-mask method. The bounding box method was found to be the most precise and is highly promising for applications in high resolution regional soft-tissue strain measurements.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".