Semi-Automatic Snake-Based Segmentation of Carotid Artery Ultrasound Images
Bibliographic record
Abstract
Carotid ultrasound imaging is one of the clinical diagnostic procedures that can be employed to detect plaque buildup at the carotid artery walls. It is an inexpensive and non-invasive procedure that has no known side effects. Yet, the acquired ultrasound images have poor quality and contain a lot of noise. Active contouring segmentation techniques (also known as snakes or deformable model) are characterized by their robustness to both image noise and boundary gaps. Hence, they are suitable to be used to segment noisy poor quality ultrasound images. One of the major issues of active contouring methods is their sensitivity to the initial contour that is provided by the user. Unless it is drawn close enough to the actual contour, it may lead to unsatisfactory results. Thus, most active contour algorithms require considerable user interaction to provide a good initial contour. This paper presents an efficient algorithm for extracting carotid artery lumens in ultrasound images. It starts by utilizing a rule-based scheme to generate an initial contour for the lumen. This contour is refined using a snake scheme, after carefully adjusting its energies. Our algorithm reduces the user interaction, as the user is only required to place a seed point inside the region of interest. It is worth mentioning that our proposed initial contour generation scheme can be easily integrated as an independent module with any active contouring algorithm. Sensitivity, precision rate, and overlap ratio are utilized to assess the performance of the proposed scheme. The results show that the extracted initial and final contours have a good overlap with contours that are manually segmented by an experienced clinician.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".