Real Time Three-Dimensional image processing with Application to Automated Cell Surgery Visual Servoing
Bibliographic record
Abstract
The locations of the target cell, its internal structures and surgical tools are essential to achieve visual servoing control for automated cell surgery. Real time image feedback allows the controller to adjust promptly for location variation due to motion of the cell when in contact with surgery tools. This paper uses Z-stack images of a 2-blastomere mouse embryo cell to develop a novel real time three-dimensional (3D) image processing algorithm. The proposed algorithm computes the centroid of each embryo blastomere and surgical tool tip in 3D image-plane coordinate. 3D Canny edge detector processes the embryo to produce a segmented 3D model. By converting the 3D model into a point cloud, the centroid of each blastomere is then estimated using an unsupervised machine learning technique, k-means clustering. For the surgical tool, 2D Canny edge detector is used in this case to compress computation time. The surgical tool tip location is selected as the furthest point away from the image edge where surgical tool appears. With 6.1μm computation variation and 2.4Hz update frequency, the proposed algorithm is suitable to perform automated cell surgery using visual servoing, especially Image-Based Visual Servoing (IBVS), with the obtained image-plane locations in the 3D image. The proposed algorithm has also shown theoretical potential to be implemented into other embryo development stages.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".