Non-uniform object counting method in large-format pyramid images applied to CD31 vessel counting in whole-mount digital pathology sections
Bibliographic record
Abstract
Whole-mount pathology imaging has the potential to revolutionize clinical practice by preserving context lost when tissue is cut to fit onto conventional slides. Whole-mount digital images are very large, ranging from 4GB to greater than 50GB, making concurrent processing infeasible. Block-processing is a method commonly used to divide the image into smaller blocks and process them individually. This approach is useful for certain tasks, but leads to over-counting objects located on the seams between blocks. This issue is exaggerated as the block size decreases. In this work we apply a novel technique to enumerate vessels, a clinical task that would benefit from automation in whole-mount images. Whole-mount sections of rabbit VX2 tumors were digitized. Color thresholding was used to segment the brown CD31- DAB stained vessels. This vessel enumeration was applied to the entire whole-mount image in two distinct phases of block-processing. The first (whole-processing) phase used a basic grid and only counted objects that did not intersect the block’s borders. The second (seam-processing) phase used a shifted grid to ensure all blocks captured the block-seam regions from the original grid. Only objects touching this seam-intersection were counted. For validation, segmented vessels were randomly embedded into a whole-mount image. The technique was tested on the image using 24 different block-widths. Results indicated that the error reaches a minimum at a block-width equal to the maximum vessel length, with no improvement as the block-width increases further. Object-density maps showed very good correlation between the vessel-dense regions and the pathologist outlined tumor regions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".