An Automated Tool to Assess Air Space Size in Histopathology Images of Lung Tissue
Bibliographic record
Abstract
The mean linear intercept (MLI) score is a useful and common approach for quantifying lung structure in histopathological images. In this paper, we describe a computer tool to determine the MLI score in a fully automated manner. A multiresolution semantic segmentation approach is used to segment the various structures within whole slide images (WSIs) of the lungs. Next, multiple field-of-view (FOV) images from the original WSI and masks are extracted. The extracted FOVs are screened, rejecting images that contain bronchi or blood vessels within the region of interest and accepting those that remain. The accepted FOVs are then used to calculate the MLI score using an intersection counting approach. The automated tool was tested using 20 WSIs from mice that were exposed to one of four conditions that affected their lung structure. The root-mean-squared deviation between the MLI score of our proposed method and a human rater was 5.73 (standard deviation 5.65), and there was a very strong correlation (r=0.9931). The proposed automated tool provides an efficient, accurate, and accessible method that could replace current manual and semi-automated techniques.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".