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 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 | 0.000 | 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".