Automatic CNN-based 3D/2D non-rigid registration platform for fast 3D femur reconstruction and clinical 3D measurements from Bi-planar radiographs
Bibliographic record
Abstract
Purpose This paper presents an automatic 3D/2D non-rigid registration method for fast 3D reconstruction and clinical measurements of the femur. Approach The proposed CNN cascade-based 3D/2D registration platform comprises three major steps to fit a generic 3D femur model into 2D bi-planar EOS® radiographs: 1) Pose estimation ( C N N P o s e ) - a combination of Principal Component Analysis (PCA) and CNN-based 3D/2D similarity registration; 2) 3D shape deformation ( C N N S h a p e ) - a CNN-based 3D displacement estimation of handles followed by Moving Least Square (MLS) shape deformation to extend an as-rigid-as-possible deformation to the entire bone, 3) 3D scale deformation ( C N N S c a l e ) - a CNN-based 3D scale ratio estimation of handles followed by MLS-based model rescaling. Results The accuracy of the method is evaluated in comparison to, first, a clinically proved semi-automatic method on 15 patients, and second, Computerized Tomography CT scans of five new patients. In the first validation, the mean ± standard deviation (STD) of the Root Mean Square of point-to-surface distance (RMS-P2S) error is 0.88 ± 0.29 mm. For the second validation, the mean ± STD of RMS-P2S error is 2.70 ± 0.39 mm. Four clinical measurements of the reconstructed 3D femurs are computed and compared with the first validation set. For each clinical measurement, the Mean Absolute Errors (MAE) is below 1 mm or 1°. Conclusions The presented automatic CNN cascade-based framework efficiently registers the generic 3D femur models into bi-planar radiographs. The CNN-based 3D handles displacement and scale estimation eliminates manual-annotations and user-interventions for MLS deformation while maintaining accuracy and speed. This system is applicable for other bones such as the tibia.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".