Watershed deconvolution for cell segmentation
Bibliographic record
Abstract
Cell segmentation and/or localization is the first stage of a (semi)automatic tracking system. We addressed the cell localization problem in our previous work where we characterized a typical blood stem cell in a microscopic image as an approximately circular object with dark interior and bright boundary. We also addressed the modelling of adjacent and dividing cells in our previous work as a deconvolution method to model individual blood stem cell as well as adjacent and dividing blood stem cells where an optimization algorithm was combined with a template matching method to segment cell regions and locate the cell centers. Our previous cell deconvolution method is capable of modelling different cell types with changes in the model parameters. However in cases where either a complex parameterized shape is needed to model a specific cell type, or in place of cell center localization, an exact cell segmentation is needed, this method will not be effective. In this paper we propose a method to achieve cell boundary segmentation. Considering cell segmentation as an inverse problem, we assume that cell centers are located in advance. Then, the cell segmentation will be solved by finding cell regions for optimal representation of cell centers while a template matching method is effectively employed to localize cell centres.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".