Automated mean linear intercept measurement: quantifying bias and parameter sensitivity in lung morphometry
Bibliographic record
Abstract
Abstract Objective. The mean linear intercept (MLI) is often used in lung morphometry; however, its assessment is labor-intensive, time-consuming, and prone to systematic biases when using the conventional indirect method. This study examines the inherent systematic biases in the indirect method, and explores the differences between the two methods, including how methodological parameters, such as the number of accepted field-of-view (FOV) images and guideline length, affect the measurement. Approach. We developed an automated MLI measurement system that uses a multiresolution semantic segmentation model. The system enables both indirect and direct MLI methods and allows for controlled variation of measurement parameters. The number of accepted FOVs was varied from 10 to 1000, and the guideline length from 39 to 702 pixels (19.4–349.5 µ m). Main results. The indirect method consistently overestimated MLI due to Septa Bias and Partial Chord Bias. The standard error of MLI decreases with more accepted FOV images, and the direct method consistently yielded a lower standard error than the indirect method. Short guideline lengths (<135.9 µ m) have a large impact on the indirect method, whereas the direct method is relatively insensitive to this parameter. Significance. The automated MLI measurement system improves the efficiency over human raters and enables higher precision by leveraging the advantages of the direct method (e.g. lower standard error, low sensitivity to guideline length) and the analysis of a larger number of FOV images. Moreover, the segmentation model used in the system is demonstrated to be accurate, which can facilitate the development of advanced morphometry techniques.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".