An Automatic System for Manipulation and Rotation of Early Stage 2-Cell Mouse Embryos
Bibliographic record
Abstract
Micromanipulation of biological cells such as embryos during preimplantation genetic diagnosis (PGD) requires delicate handling of cells. Specifically, accurate control of the embryo orientation is essential to gain a more favourable position for zona breaching and blastomere biopsy, ensuring embryo survival. Manual embryo reorientation is achieved by aspirating and releasing the embryo arbitrarily using a vacuum-equipped micropipette until the embryo reorients itself in a favourable manner. Unfortunately, this trial and error approach heavily relies on the operator’s skill, ultimately reducing biopsy success rates due to reduced precision in reorientation and ablation. In this study, an automatic image-based feedback orientation controller for early-stage embryos, using conventional IVF lab equipment, is proposed to automate the process of cell reorientation. Rotation of the embryo is achieved by rolling the embryo using a micropipette which grasps the embryo but allows slippage to permit the embryo to roll while in contact with a glass slide substrate. The substrate is mounted on a variable speed x,y stage which is controlled using one of two image-based rotation controllers investigated. The proposed control algorithms estimate the angular velocity of the embryo to determine the rotation angle, creating an error signal when differenced from the reference angle to drive a PID controller that changes the speed of the substrate. The first proposed controller uses the optical flow of the image feed to detect the cell rotation event and uses the kinematic model of the setup to determine the rotation angle. The second proposed controller uses the optical flow as a feedback signal in real-time to estimate the embryo rotation angle. Experimental results with both proposed controllers demonstrate accurate reorientation of the blastomeres, to lay the first steps towards a fully automated cell manipulation system.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".