Optimized digital counting colonies of clonogenic assays using ImageJ software and customized macros: Comparison with manual counting
Bibliographic record
Abstract
PURPOSE: To develop a digital method for counting colonies that highly replicates manual counting. MATERIALS AND METHODS: Breast cancer cells were treated with trastuzumab-conjugated gold nanoparticles in combination with X-ray irradiation, (111)In labeled trastuzumab, or γ-radiation, followed by clonogenic assays. Colonies were counted manually or digitally using ImageJ software with customized macros. Key parameters, intensity threshold and minimum colony size, were optimized based on three preliminary manual counts or blindly chosen. The correlation of digital and manual counting and inter- and intra-experimenter variability were examined by linear regression. Survival curves derived from digital and manual counts were compared by F-test (P < 0.05). RESULTS: Using optimized parameters, digital counts corresponded linearly to manual counts with slope (S) and R(2) value close to 1 and a small y-intercept (y(0)): SK-BR-3 (S = 0.96 ± 0.02, R(2) = 0.969, y(0) = 5.9 ± 2.2), MCF-7/HER2-18 (S = 0.98 ± 0.03, R(2) = 0.952, y(0) = 0.74 ± 0.47), and MDA-MB-231 cells (S = 1.00 ± 0.02, R(2) = 0.995, y(0) = 3.3 ± 4.5). Both reproducibility and repeatability of digital counts were better than the manual method. Survival curves generated from digital and manual counts were not significantly different; P-values were 0.3646 for SK-BR-3 cells and 0.1818 for MCF-7/HER2-18 cells. Using blind parameters, survival curves generated by both methods showed some differences: P-values were 0.0897 for SK-BR-3 cells and 0.0024 for MCF-7/HER2-18 cells. CONCLUSIONS: The colony counting using ImageJ and customized macros with optimized parameters was a reliable method for quantifying the number of colonies.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".