A Multi-Step Algorithm for Measuring Airway Luminal Diameter and Wall Thickness in Lung CT Images
Bibliographic record
Abstract
Accurate measurements of airway diameter and wall thickness are important parameters in understanding numerous pulmonary diseases. Here, we describe an automated method of measuring small airway luminal diameter and wall thickness over numerous contiguous computed tomography (CT) images. Using CT lung images from 22 patients and an airway phantom, a seeded region-growing algorithm was first applied to identify the lumen of the airway. The result was applied as an initial region for boundary determination using the level set method. Once found, subsequent algorithmic expansion of the luminal border was used to calculate airway wall thickness. This algorithm automatically evaluates neighboring slices of the airway and measures the airway luminal diameter and wall thickness. This approach also detects airway bifurcations. Our new procedure provides rapid, automated, accurate, and clinically important lung airway measurements that would be useful to radiologists who use CT images for pulmonary disease assessment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".