Segmentation and Quantitative Analysis of Normal and Apoptotic Cells from Fluorescence Microscopy Images
Bibliographic record
Abstract
Accurate and fast quantitative analysis of living cells from fluorescence microscopy images is useful for evaluations of experimental outcomes and cells culture protocols. An algorithm is developed in this work to automatically segment and discern apoptotic cells from normal cells. A coarse segmentation algorithm is proposed as a pre-filtering step that combines a range filter with a marching square method. This step provides approximate coordinates of cells’ positions in a two-dimensional matrix used to store cells’ image. With this information, the active contours without edges method is applied to identify cells’ boundaries and subsequently it is possible to extract the mean value of intensity within the cellular regions, the variance of pixels’ intensities in the vicinity of cells’ boundaries and the lengths of the boundaries. These morphological features are then employed as inputs to a support vector machine (SVM) classifier that is trained to distinguish apoptotic from normal viable states of cells. The algorithm is shown to be efficient in terms of computational time, quantitative analysis and differentiation accuracy, as compared to the use of the active contours method without the proposed coarse segmentation step.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".