Segmentation of atherosclerotic plaque components in ultrasonic B-mode images using a multiphase Bayesian level-set
Bibliographic record
Abstract
Partitioning an atheromatous carotid plaque into its main biological components can be a valuable tool to assess its vulnerability. One could compute textural or elasticity features on each component to describe them. In this paper, we propose a fully automated segmentation method, based on statistical properties of the ultrasound backscattered envelope signals, to classify plaque pixels into a fixed number of components. The echogenicity of each plaque was modeled as a mixture of 2 Nakagami distributions leading to 2 main components. The mixture parameters were at first estimated with an Expectation Maximization algorithm (EM). Each class of the partition was then initialized using the Maximum Likelihood segmentation (ML). The optimal partition of the plaque area was then found using a level-set formulation of the Maximum A Posteriori (MAP) estimator with a spatial cohesion prior. Nakagami mixture parameters were extracted from those components. Uncompressed B-mode sequences of 8 symptomatic and 13 asymptomatic subjects were analyzed for a total of 42 plaque sequences. We found that the Nakagami parameters were able to distinguish symptomatic from asymptomatic patients with a significant p-value. Further works including elasticity mapping on each component are in progress and might lead to new indexes of vulnerability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".