Automated Real-time 3D Visual Servoing Control of Single Cell Surgery Processes
Bibliographic record
Abstract
In this article, we propose an automated real-time 3D visual servoing control of single cell surgery processes, which integrates the automation of micromanipulator calibration process, cell initial setup process, and the visual serving control surgery operation. During the cell initial setup process, a deep learning-based object detection algorithm is developed to compute the cell location in real-time for cell surgery. These coordinates are used as feedback derived two-dimensional (2D) images acquired under low microscope magnification for the motion control of a motorized microscope stage. Visual serving control of cell surgery process is achieved through a cell blastomere centroid three-dimensional (3D) localization algorithm. This algorithm computes in real-time centroid position feedback of the blastomere of interest in the 3D image coordinate from 3D Z-stack images. As the most important function within the overall 3D localization algorithm, a 3D edge detection algorithm is used to extract the edges inside the blastomere. The extracted edges are converted into 3D point cloud which is used to determine the centroid location through 3D K-mean clustering algorithm. A position based visual servoing (PBVS) controller is then used to maneuver the micropipette under closed-loop control enabling various cell surgery procedures to be carried out.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".