Evaluation of an algorithm for semiautomated segmentation of thin tissue layers in high-frequency ultrasound images
Bibliographic record
Abstract
An algorithm consisting of speckle reduction by median filtering, contrast enhancement using top- and bottom-hat morphological filters, and segmentation with a discrete dynamic contour (DDC) model was implemented for nondestructive measurements of soft tissue layer thickness. Algorithm performance was evaluated by segmenting simulated images of three-layer phantoms and high-frequency (40 MHz) ultrasound images of porcine aortic valve cusps in vitro. The simulations demonstrated the necessity of the median and morphological filtering steps and enabled testing of user-specified parameters of the morphological filters and DDC model. In the experiments, six cusps were imaged in coronary perfusion solution (CPS) then in distilled water to test the algorithm's sensitivity to changes in the dimensions of thin tissue layers. Significant increases in the thickness of the fibrosa, spongiosa, and ventricularis layers, by 53.5% (p < 0.001), 88.5% (p < 0.001), and 35.1% (p = 0.033), respectively, were observed when the specimens were submerged in water. The intraobserver coefficient of variation of repeated thickness estimates ranged from 0.044 for the fibrosa in water to 0.164 for the spongiosa in CPS. Segmentation accuracy and variability depended on the thickness and contrast of the layers, but the modest variability provides confidence in the thickness measurements.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".