Segmentation of plaques in sequences of ultrasonic B-mode images of carotid arteries based on motion estimation and Nakagami distributions
Bibliographic record
Abstract
The goal of this work was to perform a segmentation of atherosclerotic plaques in view of evaluating its burden and to provide boundaries for computing properties such as the plaque elasticity distribution (elastogram). The echogenicity of a region of interest comprising the plaque, the lumen, and the adventitia in an ultrasonic B-mode image was modeled by a mixture of three Nakagami distributions. To each of these three tissues corresponds a specific weighted sum of these three distributions, which yields the likelihood of a Bayesian model. The prior of that model includes a geometrical smoothness constraint, as well as an original spatio-temporal cohesion constraint, based on the estimation of the motion field of the plaque in the video sequence. The Maximum A Posteriori (MAP) of the proposed model was computed with a variant of the Exploration/Selection (ES) algorithm. The starting point is a manual segmentation of the first frame. The main contribution of this paper is the estimation of the motion field and its integration into the prior of the Bayesian model. The proposed method was quantitatively compared with manual segmentations of all frames by an expert technician. Various measures were used for this evaluation, including the sensitivity and specificity of the receiver operating characteristic (ROC) analysis, and the error of area of the plaque. Results were evaluated on 21 sequences of 18 symptomatic patients (for a total of 2078 images). We report a sensitivity of 87.7 ± 5.9%, a specificity of 94.6 ± 3.2%, a kappa index of 88.1 ± 4.7%, an error of the plaque area of -0.79 ± 8.0%, and an absolute error of the plaque area of 6.61 ± 4.8%, which compare favorably with previous studies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".