Validation of high-frequency ultrasound measurements of tissue layer thickness
Bibliographic record
Abstract
High-frequency ultrasound imaging enables nondestructive measurement of layer thickness in tissue specimens. These measurements are valuable for mechanical testing of soft biomaterials. This paper demonstrates a method for assessing the accuracy of high-resolution ultrasonic thickness estimates. Three-dimensional images of six porcine aortic valve cusps were acquired in vitro using a 40 MHz ultrasound system with 40×80×80 μm3 spatial resolution. The cusps were then frozen in liquid nitrogen, sectioned into 10-μm slices, and micrographs of one slice from each specimen were acquired at 4× magnification. The two-dimensional micrographs were registered to the three-dimensional ultrasound images using a cross-correlation method. The boundaries of the fibrosa, spongiosa, and ventricularis layers were segmented in both sets of images using an active contour model. The average thicknesses of the tissue layers in the registered images were estimated and the absolute differences of the optical and ultrasonic estimates were computed. The absolute differences were 55.8±22.6 μm (mean ± standard deviation), 23.5±14.3 μm, and 22.7±17.2 μm for the fibrosa, spongiosa, and ventricularis, respectively. The measurement differences are comparable to the axial resolution of the ultrasound system and are not significant as determined by t-tests (p>0.30 for each layer).
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".