Segmentation of carotid artery in ultrasound images
Bibliographic record
Abstract
Segmentation of carotid artery lumen in 2D and 3D ultrasonography is an important step in evaluating arterial disease severity and finding vulnerable artherosclerotic plaques susceptible to rupture causing stroke. Because of the complexity of anatomical structures, noise as well as the requirement of accurate segmentation, interactions are necessary between observers and computer segmentation process. We describe a segmentation algorithm based on a discrete dynamic model approach with only one seed point to guide the initialization of the deformable model for each lumen cross-section. With one seed, the initial contour of the deformable model is generated using the entropy map of the original image and mathematical morphology operations. The deformable model is driven to fit the lumen contour by an internal force and an external force that are calculated respectively with geometrical properties of deformed contour and with the image gray level features. We also introduce a set of metrics based on a contour probability distribution function for evaluating the accuracy and variability of the interactive segmentation algorithm. These metrics provide a complete performance evaluation of an interactive segmentation algorithm and a means for comparing different algorithm settings. Seven images of the common, internal and external carotid arteries were chosen to test the segmentation algorithm. The average position error and average variability of the boundary segmentation result are 0.2 mm and 0.25 mm.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".