A multiscale graph cut approach to bright-field multiple cell image segmentation using a Bhattacharyya measure
Bibliographic record
Abstract
Automatic segmentation of bright-field cell images is important to cell biologists, but is difficult to achieve due to the complex nature of the cells in bright-field images (poor contrast, broken halo, missing boundaries). The standard segmentation techniques, such as the level set method and active contours, are not able to overcome these features of bright-field images. Consequently, poor segmentation results are produced. In this paper, we present a robust segmentation method, which combines the techniques of graph cut, multiresolution, and Bhattacharyya measure, performed in a multiscale framework, to locate multiple cells in bright-field images. The issue of low contrast in bright-field images is addressed by determining the difference in intensity profiles of the cells and the background. The resulting segmentation on the entire image frame provides global information. Then a local segmentation at different regions of interest is performed to obtain finer details of the segmentation result. We illustrate the effectiveness of the method by presenting the segmentation results of C2C12 (muscle) cells in bright-field images.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".