Semi-automatic segmentation of major aorto-pulmonary collateral arteries (MAPCAs) for image guided procedures
Bibliographic record
Abstract
Manual segmentation of pre-operative volumetric dataset is generally time consuming and results are subject to large inter-user variabilities. Level-set methods have been proposed to improve segmentation consistency by finding interactively the segmentation boundaries with respect to some priors. However, in thin and elongated structures, such as major aorto-pulmonary collateral arteries (MAPCAs), edge-based level set methods might be subject to flooding whereas region-based level set methods may not be selective enough. The main contribution of this work is to propose a novel expert-guided technique for the segmentation of the aorta and of the attached MAPCAs that is resilient to flooding while keeping the localization properties of an edge-based level set method. In practice, a two stages approach is used. First, the aorta is delineated by using manually inserted seed points at key locations and an automatic segmentation algorithm. The latter includes an intensity likelihood term that prevents leakage of the contour in regions of weak image gradients. Second, the origins of the MAPCAs are identified by using another set of seed points, then the MAPCAs' segmentation boundaries are evolved while being constrained by the aorta segmentation. This prevents the aorta to interfere with the segmentation of the MAPCAs. Our preliminary results are promising and constitute an indication that an accurate segmentation of the aorta and MAPCAs can be obtained with reasonable amount of effort.
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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 | 0.003 |
| 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 | 0.001 | 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".