Ultrasound B‐scan image simulation, segmentation, and analysis of the equine tendon
Bibliographic record
Abstract
PURPOSE: The hypothesis is that an imaging technique based on decompression and segmentation of B-scan images with morphological operators can provide a measurement of the integrity of equine tendons. METHODS: Two complementary approaches were used: (i) Simulation of B-scan images to better understand the relationship between image properties and their underlying biological structural contents and (ii) extraction and quantification from B-scan images of tendon structures identified in step (i) to diagnose the status of the superficial digital flexor tendon (SDFT) by using the proposed imaging technique. RESULTS: The simulation results revealed that the interfascicular spaces surrounding fiber fascicle bundles were the source of ultrasound reflection and scattering. By extracting these fascicle bundles with the proposed imaging technique, quantitative results from clinical B-scan images of eight normal and five injured SDFTs revealed significant differences in fiber bundle number and areas: mean values were 50 (+/- 11) and 1.33(+/- 0.36) mm2 for the normal SDFT data set. Different values were observed for injured SDFTs where the intact mean fiber bundle number decreased to 40 (+/- 7) (p = 0.016); inversely, mean fiber bundle areas increased to 1.83 (+/- 0.25) mm2 (p = 0.008), which indicate disruption of the thinnest interfascicular spaces and of their corresponding fiber fascicle bundles where lesions occurred. CONCLUSIONS: To conclude, this technique may provide a tool for the rapid assessment and characterization of tendon structures to enable clinical identification of the integrity of the SDFT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".