Comparison of U-Net Based Models for Human Embryo Segmentation
Bibliographic record
Abstract
The quality of human embryos produced during in vitro fertilization is conventionally graded by clinical embryologists and this process is time-consuming and prone to human error. Artificial intelligence methods may be used to grade images captured by time-lapse microscopy (TLM). Segmentation of embryos from the background of TLM images is an essential step for embryo quality assessment as the background of the embryo has various artifacts which may mislead the grading algorithms. In this study, we performed a comparative analysis of automated day-5 human embryo (blastocyst) image segmentation methods based on deep learning. Four fully convolutional deep models, including U-Net and its three variants, were created using the combination of two gradient descent-based optimizers and two-loss functions and compared to our proposed model. The experimental results on the test set confirmed that our customized Dilated Inception U-Net model with Adam optimizer and Dice loss outperformed other U-Net variants with Dice coefficient, Jaccard index, accuracy, and precision of 98.68%, 97.52%, 99.20%, and 98.52%, respectively.
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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 | 0.002 | 0.003 |
| 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 | 0.000 |
| 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".