Combining Laplacian eigenmaps and vesselness filters for vessel segmentation in X-ray angiography
Bibliographic record
Abstract
Automatic vessel outline delineation from X-ray angiography is highly useful to cardiologists during interventional procedures, especially to measure clinical indices such as vessel diameters, perimeters and areas. The challenges of obtaining a fully automatic segmentation are plentiful: radiographic noise, irregular injection of contrast agent, vessel overlap, etc. While vesselness filters were proposed to detect probable vessel-like shapes, such techniques often fail to recover prominent vessels in a cluttered background, and may obtain irregular shapes when artifacts are present. In this study, we propose a novel approach to segment vessel-like structures, which combines vesselness filters and Laplacian eigenmaps. Our technique finds automatically a global optimum solution for the image segmentation problem. By using both vesselness and Laplacian features, this approach can recognize vessel-like shapes in the background, while preserving the regularity of the extracted shapes. A visual and quantitative evaluation of the proposed approach, on both simulated images and pediatric patient X-ray angiography data, demonstrates its usefulness and efficiency.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".