An Analysis Algorithm for Measuring Airway Lumen and Wall Areas from High-Resolution Computed Tomographic Data
Bibliographic record
Abstract
High-resolution computed tomography (HRCT) has been used to examine airway narrowing. We developed an automated computed tomographic image analysis algorithm (computed tomographic airway morphometry; CTAM) to measure airway lumen area (Ai ), airway wall area (Awa), and airway angle of orientation. Tubes of varying size were embedded in Styrofoam and then scanned at angles between 0 degrees and 50 degrees to assess the accuracy of measurements made with CTAM. Two excised pig lungs were fixed in inflation, sectioned, and scanned. Ai and Awa were measured planimetrically from the cut surfaces to optimize CTAM measurement parameters. In CTAM, Ai was defined according to an airway-size-dependent threshold value, and total Awa was determined through a score-guided erosion method. Results were compared with measurements made through a previously validated method (manual method). CTAM provided accurate measurements of the tubes' Ai values at all angles; Awa was overestimated in direct relation to airway size. The manual method underestimated Ai and overestimated Awa in a manner directly related to airway size as well as to airway angle of orientation. In the excised lung, the mean errors of Ai and Awa measurements made with CTAM were 0.52 +/- 0.24 mm(2) and 0.17 +/- 0.32 mm(2) (mean +/- SEM), respectively. Ai errors with the manual method were similar, but Awa was overestimated to a greater degree (6.3 +/- 0.38 mm(2); p < 0.01) and the error was proportional to Awa (r = 0.64; p < 0.01). CTAM allows accurate measurements of airway dimensions and angle of orientation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".