Tissue Characterization of Equine Tendons With Clinical B-Scan Images Using a Shock Filter Thinning Algorithm
Bibliographic record
Abstract
The fiber bundle density (FBD) calculated from ultrasound B-scan images of the equine superficial digital flexor tendon (SDFT) can serve as an objective measurement to characterize the three metacarpal sites of normal SDFTs, and also to discriminate a healthy SDFT from an injured one. In this paper, we propose a shock filter algorithm for the thinning of hyper-echoic structures observed in B-scan images of the SDFT. This algorithm is further enhanced by applying closing morphological operations on filtered images to facilitate extraction and quantification of fiber bundle fascicles. The mean FBD values were calculated from a clinical B-scan image dataset of eight normal and five injured SDFTs. The FBD values measured at three different tendon sites in normal cases show a highest density on the proximal site (five cases out of eight) and a lowest value on the distal part (seven cases out of eight). The mean FBD values measured on the entire tendon from the whole B-scan image dataset show a significant difference between normal and injured SDFTs: 51 (±9) for the normal SDFTs and 39 (±7) for the injured ones (p = 0.004) . This difference likely indicates disruption of some fiber fascicle bundles where lesions occurred. To conclude, the potential of this imaging technique is shown to be efficient for anatomical structural SDFT characterizations, and opens the way to clinically identifying the integrity of SDFTs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".