Enhanced X‐ray image segmentation method using prior shape
Why this work is in the frame
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Bibliographic record
Abstract
An enhanced version of a segmentation algorithm applied in X‐ray images using a prior shape and a straightened boundary image (SBI) is proposed. In the SBI method, the boundary of the target object is extracted with a constant width along the prior shape and transformed to a rectangular image in which the edges are straightened. A new minimal path algorithm is proposed and applied to SBI minimising a cost function to select the best path corresponding to the edges of the target object. The cost function is calculated based on all possible paths from each pixel to the beginning of the image while lowering the computational complexity. Comparing with previous methods, the proposed method removes artefacts and provides clearer and smoother edges even when the prior shape is far from the target object. The method is also less sensitive to the initial positioning of the prior shape model.
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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 | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 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