Automated segmentation of lung airway wall area measurements from bronchoscopic optical coherence tomography imaging
Bibliographic record
Abstract
Chronic Obstructive Pulmonary Disease (COPD) affects almost 600 million people and is currently the fourth leading cause of death worldwide. COPD is an umbrella term for respiratory symptoms that accompany destruction of the lung parenchyma and/or remodeling of the airway wall, the sum of which result in decreased expiratory flow, dyspnea and gas trapping. Currently, x-ray computed tomography (CT) is the main clinical method used for COPD imaging, providing excellent spatial resolution for quantitative tissue measurements although dose limitations and the fundamental spatial resolution of CT limit the measurement of airway dimensions beyond the 5th generation. To address this limitation, we are piloting the use of bronchoscopic Optical Coherence Tomography (OCT), by exploiting its superior spatial resolution of 5-15 micrometers for in vivo airway imaging. Currently, only manual segmentation of OCT airway lumen and wall have been reported but manual methods are time consuming and prone to observer variability. To expand the utility of bronchoscopic OCT, automatic and robust measurement methods are required. Therefore, our objective was to develop a fully automated method for segmenting OCT airway wall dimensions and here we explore several different methods of image-regeneration, voxel clustering and post-processing. Our resultant automated method used K-means or Fuzzy c-means to cluster pixel intensity and then a series of algorithms (i.e. cluster selection, artifact removal, de-noising) was applied to process the clustering results and segment airway wall dimensions. This approach provides a way to automatically and rapidly segment and reproducibly measure airway lumen and wall area.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".