Self-Calibration of Biplanar Radiographic Images Through Geometric Spine Shape Descriptors
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Bibliographic record
Abstract
This paper presents a novel self-calibration method of an X-ray scene applied for the 3-D reconstruction of the scoliotic spine. Current calibration techniques either use a cumbersome calibration apparatus or depend on manually identified landmarks to determine the geometric configuration, thus limiting routine clinical evaluation. The proposed approach uses high-level information automatically extracted from biplanar X-rays to solve the radiographic scene parameters. We first present a segmentation method that takes into account the variable appearance and geometry of a scoliotic spine in order to isolate and extract the silhouettes of the anterior vertebral body. By incorporating prior anatomical information through a Bayesian formulation of the morphological distribution, a multiscale spine segmentation framework is proposed for scoliotic patients. An iterative nonlinear optimization procedure, integrating a 3-D visual hull reconstruction and geometrical torsion properties of the spine, is then applied to globally refine the geometrical parameters of the 3-D viewing scene and obtain the optimal 3-D reconstruction. An experimental comparison with data provided from reference synthetic models yields similar accuracy on the retroprojection of low-level primitives such as anatomical landmarks identified on each vertebra (2.2 mm). Results obtained from a clinical validation on 60 pairs of uncalibrated digitized X-rays of adolescents with scoliosis show that the 3-D reconstructions from the new system offer geometrically accurate models with insignificant differences for 3-D clinical indexes commonly used in the evaluation of spinal deformities. The reported experiments demonstrate a viable and accurate alternative to previous reconstruction techniques, offering the first automatic approach for routine 3-D clinical assessment in radiographic suites.
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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 | 0.001 | 0.003 |
| 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 it