2D/3D Reconstruction of The Distal Tibiofibular Joint from Biplanar Radiographs Using Deep Learning Registration and Statistical Shape and Intensity Model
Bibliographic record
Abstract
In the present study, we introduce a novel approach for the three-dimensional (3D) reconstruction of the distal tibiofibular joint shape from its two-dimensional (2D) biplanar radiographs. An independent Statistical Shape and Intensity Model is used to generate radiographs using the mean model and its associated variations. These generated Anteroposterior and Lateral images are subsequently utilized to train a Deep Learning-based regressor network, allowing for the instantaneous determination of the joint’s shape, intensity, and pose parameters in an end-to-end fashion. This leads to expeditious reconstruction of the 3D surface of the anatomical tibiofibular joint's 3D surface from its 2D radiographs. Our methodology was applied to reconstruct the distal tibia and distal fibula simultaneously. The results demonstrate that the Deep Network Regressor is proficient in reconstructing the surface of the entire structure with an average error of 0.63 millimeters.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".