SU‐GG‐I‐46: An Automatic Region Detection Algorithm for Analyzing Module 1 of the ACR CT Accreditation Phantom
Bibliographic record
Abstract
Purpose: A region identification algorithm has been developed to facilitate the automatic analysis of Module 1 of the ACR CT accreditation phantom. Method and Materials: The ACR CT accreditation phantom was scanned using a GE Lightspeed QX/i scanner, and a preliminary algorithm was developed to identify the centers of the air and bone‐mimicking areas in Module 1 of the phantom. The air and the “bone” regions in the CT image were first isolated by binarizing the image with a threshold of −500 and 800 HU, respectively. Next, a Canny edge detector was applied to the air‐only binary images to detect the edges, and the center of the phantom was then identified using a Hough‐based circular object detection algorithm. Lastly, the edge of the phantom was removed and the center of the air‐only region is subsequently detected. A similar procedure was used to identify the center of the “bone” region. Results: To assess the algorithm, the CT image from Module 1 of the ACR CT phantom was computationally rotated to various orientations. The centers of the air and “bone” regions were then located using the preliminary algorithm. The resulting averages for both regions were very reproducible (within 1 HU). Conclusion: The preliminary results above have demonstrated that the algorithm can robustly identify the centers of the air and bone regions. Once the centers of these two regions are located, the required ROIs can be extracted from the original CT image, and the required statistics computed. Since the phantom is a rigid object, this implies that the other areas of interest in Module 1 can be easily identified. This will be demonstrated in the presentation.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".