Identification of Anatomical Landmarks for Registration of CT and Ultrasound Images in Computer-Assisted Shoulder Arthroscopy
Bibliographic record
Abstract
This paper presents a phantom study that was conducted for an ultrasound-guided shoulder arthroscopy navigation system. The navigation system uses a surface model generated from pre-operative computed tomography images, which has to be registered to the patient during the procedure. The goal of this study was to determine the optimal regions on the scapula bone of the shoulder to achieve an acceptable registration. Experiments were performed to examine the robustness and suitability of these optimal regions by testing the sensitivity to variations in the initial alignment for two different registration algorithms, namely iterative closest point and sequential least squares estimation technique. The fiducial registration error was analyzed and compared for all experiments. Regions spread over the entire scapula result in significantly smaller registration error (p<0.001) than regions, concentrated around the shoulder joint and thus accessible during the shoulder arthroscopy. However, the results also showed that the registration is still acceptable for the image-guided navigation system when these accessible landmarks are used.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".