Intravascular Ultrasound image segmentation: A helical active contour method
Bibliographic record
Abstract
During an Intravascular Ultrasound (IVUS) examination, a catheter with an ultrasound transducer is introduced in the body through a blood vessel and then pulled back to image a sequence of vessel cross-sections. An IVUS exam results in several hundred noisy images often hard to analyze. Hence, developing powerful automatic analysis tool would facilitate the interpretation of structures in IVUS images. In this paper we present a new IVUS segmentation method based on an original active contour model. The contour has a helical geometry and evolves like a spiral shape that is distorted until it reaches the artery lumen boundaries. Despite the use of a simple statistical model and a very sparse initialization of the snake, the algorithm converges to satisfying solutions that can be compared with much more sophisticated segmentation methods. To validate the method, we compared our results to manually traced contours and obtained an Hausdorff distance <; 0.61mm (n = 540 images) indicating the robustness of the method.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".