Application of Euclidean distance mapping for assessment of basement membrane thickness distribution in asthma
Bibliographic record
Abstract
Abnormal thickening of the airway basement membrane is one of the hallmarks of airway remodeling in asthma. The present protocols for measuring the basement membrane involve the use of stained tissue sections and measurements of the basement membrane thickness at certain intervals, followed by the calculation of the geometric mean thickness for each airway. This report describes an automated, unbiased approach which uses color segmentation to identify structures of interest on stained sections and Euclidean distance mapping to measure the thickness distribution of airway structures. This method was applied to study the thickness distribution of the basement membrane and airway epithelium in lungs donated for research from seven nonasthmatic and eight asthmatic age- and sex-matched donors. A total of 60 airways were assessed. We report that the thickness and thickness distribution of the basement membrane and airway epithelium are increased in large and small airways of asthmatics compared with nonasthmatics. Using this method we were able to demonstrate the heterogeneity in the thickness of the basement membrane and airway epithelium within individual airways of asthmatic subjects. This new computational method enables comprehensive and objective quantification of airway structures, which can be used to quantify heterogeneity of airway remodeling in obstructive lung diseases such as asthma and chronic obstructive pulmonary disease. NEW & NOTEWORTHY The described application of Euclidean distance mapping provides an unbiased approach to study the extent and thickness distribution of changes in tissue structures. This approach will enable researchers to use computer-aided analysis of structural changes within lung tissue to understand the heterogeneity of airway remodeling in lung diseases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".