Low-cost quasi-real-time elastography using B-mode ultrasound images
Bibliographic record
Abstract
A low cost, quasi real-time elastography system, displacement-gradient elastography (DGE), was developed by applying digital image correlation (DIC) method and smoothing algorithm to B-mode ultrasound images. In order to achieve quasi real-time elastogram display, a new fast pattern matching algorithm, decoupled cross-correlation (DCC), was proposed and validated. By applying the DGE to various phantoms, elastograms were generated to identify the lesion with wide variations of stiffness ratio and applied strain. The performance of DGE was qualitatively compared with those from a high-end ultrasound scanner using the elastograms of a commercial elastography breast phantom. DGE was also applied to the ultrasound images of human breast lesions in various BI-RADS categories. This study suggests that DGE may have comparable performance to conventional elastography in detecting breast cancer, while it can be easily implemented onto conventional ultrasound scanners.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".