Automation of single cell surgery in real-time using a vision-based control system
Bibliographic record
Abstract
Micromanipulation of biological cells is a challenging task that requires levels of precision and repeatability which are difficult to achieve by most human operators. Automation of these processes presents an alternative approach which is capable of high precision task execution and much higher throughput, yet with its own limitations. In this paper, we propose automation methods for the image-based visual servo control feedback and tracking of both blastomeres' motion and the motion of micromanipulators, in real-time, for blastomere microinjection. An automation procedure is developed for the microinjection or blastomere biopsy of an early stage embryonic cell. These steps involve blastomere z-stack image acquisition, blastomere feature detection (x, y, z) location, and real-time image-based visual servo control of micropipettes to hold and immobilise the embryo while a micropipette injects or biopsies the blastomere. Experimental results demonstrate acceptable precision levels while performing automation procedure in real-time.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".