A Conditional Random Field Weakly Supervised Segmentation Approach for Segmenting keratocytes Cells in Corneal Optical Coherence Tomography Images
Bibliographic record
Abstract
Keratocytes are vital for maintaining the overall health of human cornea as they preserve the corneal transparency and help in healing corneal injuries. Manual segmentation of keratocytes is challenging, time consuming and also needs an expert. Here, we propose a novel semi-automatic segmentation framework, called Conditional Random FieldWeakly Supervised Segmentation (CRF-WSS) to perform the keratocytes cell segmentation. The proposed framework exploits the concept of dictionary learning in a sparse model along with the Conditional Random Field (CRF) modeling to segment keratocytes cells in Ultra High Resolution Optical Coherence Tomography (UHR-OCT) images of human cornea. The results show higher accuracy for the proposed CRF-WSS framework compare to the other tested Supervised Segmentation (SS) andWeakly Supervised Segmentation (WSS) methods.
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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 | 0.001 | 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) | 0.001 | 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".