Computer vision to predict cell seeding coverage in re-endothelialized mouse lungs
Bibliographic record
Abstract
Transplantation of donor grafts recellularized with recipient-derived or non-immunogenic universal cells is a potential means of reducing the graft rejection and post-transplant complications in lung transplantation. Achieving a fully recellularized lung, however, remains a far-off goal and has several limitations, including inadequate cell coverage of the acellular scaffold. A key parameter for evaluating recellularization efficacy is the cell seeding coverage (CSC); the ratio of seeded cell area to the total area of the lung scaffold. To calculate the CSC from a histological image, the lung scaffold and the seeded cell areas must be quantified. In this work, the ability of semantic segmentation to accurately automate the pixel-wise analysis of histological images is investigated. Specifically, the U-Net and LinkNet models are applied to re-endothelialized mouse lung images, generating pixel-wise classifications of the lung scaffold and the seeded cell areas to calculate the CSC. Model performance when trained on complete images and on image patches is compared. The patch-based U-Net model outperformed the other models, predicting CSC with a root mean square error of 2.23 ± 0.36%, in addition to classifying lung scaffold pixels and seeded cell pixels with intersection over union scores of 77.8 ± 1.4% and 69.5 ± 1.1% respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".