Detection And Identification Of Lower-Limb Bones In Biplanar X-RAY Images With Arbitrary Field Of View And Various Patient Orientations
Bibliographic record
Abstract
Correctly detecting and identifying bones in radiographic images are the first stages of every orthopaedic procedure. This apparently simple task is mainly performed by human operators before performing more complex operations. Automatic detection and identification of bones in radiographic images, especially if the field of view and the position of the patient are not known a priori, remains a challenging task. In this paper, lower-limb bones are automatically detected and identified on biplanar X-ray images with varying fields of view and two main orientations of the patient with respect to the imaging device. The proposed method uses data augmentation to improve the training of a deep learning method to identify the lower-limb bones. We used 30 biplanar radiographs with varying fields of view to validate the proposed method. We obtained a global accuracy (mean±std) of 96.75±0.01% and a Dice coefficient of 93.85±0.02%, proving the usefulness of the proposed method.
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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 | 0.000 |
| 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".