Coupled level set approach to segment carotid arteries from 3D ultrasound images
Bibliographic record
Abstract
In this paper, we described and validated a semi-automated algorithm based on the level set method to segment the media-adventitia boundary (MAB) and lumen-intima boundary (LIB) of the carotid arteries from 3D ultrasound (3DUS) images to support the computation of carotid vessel wall volume (VWV). We incorporated local region-based and edge-based energies for the MAB segmentation, and both local and global region-based energies for the LIB segmentation. The two level set functions are coupled using a boundary separation-based energy to encourage an anatomically-motivated boundary separation between the MAB and LIB. An additional energy term attracts the boundary to pass through anchor points placed by an operator. The algorithm was evaluated with 231 2D transverse images extracted from 21 3DUS images. The algorithm gave a VWV error of 5.2%±3.9%, yielded Dice coefficients of 95.6% ± 1.5%, 92.8% ± 3.2% for the MAB and LIB, respectively, and gave sub-millimeter boundary distance errors. The coefficients of variation of VWV from the semi-automated (5.0%) and manual (3.9%) methods were not significantly different.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".