Structural method for tracking coronary arteries in coronary cineangiograms
Bibliographic record
Abstract
In this paper, a model-based tracking algorithm is implemented to track the motion of coronary arteries. This idea was first introduced by Kong et al. [10] in 1971 to assess the heart contraction using for this purpose the coronary bifurcations as natural landmarks on the epicardial surface. The method implemented here assumes that a coronary bifurcation can be represented by a simple Y geometric structure. A Fuzzy C-Means algorithm is first used to segment the coronary bifurcations. Then the segmented bifurcation is skeletonized to produce the expected Y shape. To define the Y shape geometrically, its center and the branch angles are computed. The tracking process can now take place by simply looking for a similar Y shape in the next frame and so on. Using actual cineangiograms, it is demonstrated that tracking the movement of coronaries with this geometrical approach is more accurate and robust then using a standard correlation window methodology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".