<title>Segmentation of ulcerated plaque: evaluation and optimization of a semiautomatic method for tracking the progression of carotid atherosclerosis</title>
Bibliographic record
Abstract
A semi-automatic method for segmenting carotid lumen and plaque from three-dimensional vascular ultrasound (US) images has been developed. We examine its ability to distinguish changes in carotid vessel and plaque surface morphology, such as those caused by plaque ulceration. Two stenosed vessel phantoms were imaged using a 3D US imaging system. The phantoms were identical except for the inclusion of a hemispherical cut in the side of one of the vessels, in order to simulate the development of an ulceration. Ultrasound images of the phantoms were segmented using our algorithm, then the resulting surfaces were registered to one another using a rigid-body iterative closest point (ICP) algorithm. The volume of ulceration was determined by finding the difference between the two segmented surfaces in a region of interest surrounding the ulceration. Since the true volume of the ulceration was known a priori, an optimization strategy was used to tune the deformable model to better segment the ulceration. Analysis of ulceration volume as a function of the deformable model's parameters show that 1) large ulcerations are easily identified in our test case, and 2) the model is well behaved with respect to its parameters, suggesting that an automatic strategy for volumetric optimization is feasible.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".