Highly Accurate Automated Patient-Specific 3D Bone Pose and Scale Estimation Using Bi-Planar Pose-Invariant Patches in a CNN-Based 3D/2D Registration Framework
Bibliographic record
Abstract
This paper proposes an automatic CNN-based 3D/2D registration method to achieve highly accurate and robust seven degrees of freedom (7DOF) pose and isotropic scale of a generic 3D model. This step is a key enabler for reconstructing a patient-specific 3D bone surface model from a wide range of EOS® 2D bi-planar X-rays acquired with various fields of view and patients' orientations. Based on a coarse-to-fine strategy, first a CNN-based semantic segmentation followed by a PCA-based registration are used to roughly locate the bone. Similarity in pose-invariant local patches using CNN regression models is used to refine the 3D pose. The accuracy of the method is validated on 60 bi-planar X-rays. The mean of Mean Absolute pose Errors (MAE) of 3D translations, 3D rotations, and isotropic scaling are 0.19 mm, 0.33°, and 0.05 (%), respectively. The success rate is of 100 % at MAE lower than 1 mm, 1°, and 0.1 (%).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".