Blastomere Cell Counting and Centroid Localization in Microscopic Images of Human Embryo
Bibliographic record
Abstract
The time of the first cell cleavage in the embryonic development of a human embryo is an important indicator of the embryo's potential for developing into a healthy baby. The time and synchronicity of following cleavages are also linked to the quality of an embryo. In this paper, a deep learning based framework is proposed to take on the challenging task of automatic counting and centroid localization of embryonic cells (blastomeres) in microscopic images of human embryos. In particular, ensemble of residual dilated UNet is proposed to count blastomeres and localize their centroids. Experimental results confirm that the proposed framework is capable of counting blastomeres in a densely occupied and overlapping space of human embryo by an average accuracy of 88.2% for embryos of 1 - 5 cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".