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Automated Real Time Image Based Visual Servo Control of Single Cell Surgery

2020· article· en· W3097429114 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicImage Processing Techniques and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBlastomereComputer scienceAutomationComputer visionArtificial intelligenceServo controlServoImage processingImage (mathematics)EngineeringBiologyEmbryo

Abstract

fetched live from OpenAlex

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 embryonic cell. These steps involve blastomere z-stack image acquisition, image processing to identify blastomere feature (x, y, z) location, real time image based visual servo control of micropipettes to hold and immobilize the embryo while a micropipette injects or biopsies the blastomere. Experimental results demonstrate early results of this automated procedure.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.862
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.226
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it