Improved T-Snake Model Based Edge Detection of the Coronary Arterial Walls in Intravascular Ultrasound Images
Bibliographic record
Abstract
Accurately detecting edges of the coronary arterial walls in intravascular ultrasound (IVUS) images plays an important role in quantitative assessments of the coronary artery diseases, though it may be a challenge to medical image analysis. This paper presented a method to extract the edges, main characteristics of which were summarized as: 1) The method was based on an improved topologically adaptable snake model (T- Snake model). This proposed T-Snake model could successfully address the significant problem of conventional T-Snakes when dealing with the model self-intersection. 2) The method worked in conjunction with an adaptive homomorphic spatial/temporal filtering technique. This proposed filtering technique was capable of effectively reducing the strong blood speckle noise in IVUS images. The experimental results indicated the proposed method was accurate and robust in detecting edges of the coronary arterial walls in IVUS images, as well as reproducible for sequential IVUS frames.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".