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Record W2147813587 · doi:10.1109/isbi.2008.4540955

Synthetic images of blood microcirculation to assess precision of velocity profiles by a cross-correlation method

2008· article· en· W2147813587 on OpenAlexaff
Marianne Fenech, Boris Chayer, Guy Cloutier

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicBlood properties and coagulation
Canadian institutionsUniversité de Montréal
FundersNational Institutes of Health
KeywordsMicrocirculationHematocritDigital image correlationRADIUSCross-correlationFlow velocityOptical flowBlood flowFlow (mathematics)Materials scienceMathematicsOpticsBiomedical engineeringPhysicsImage (mathematics)MechanicsMathematical analysisComputer visionComputer science

Abstract

fetched live from OpenAlex

Optical cross-correlation methods have been used to study the motion of red blood cells (RBC) in the microcirculation. To evaluate the precision of such a method to determine RBC velocity profiles, we developped a computational model of the microscopy image formation. The following steps were undertaken: (1) a mechanical model was used to mimic three dimensional RBC movements in a tubular parabolic flow; (2) at each time step, a synthetic image was built using microscopic image formation equations based on the depth of correlation of RBCs; and (3) the velocity profile was extracted by a cross-correlation algorithm applied to these synthetic images. The estimated maximum velocities extracted from the simulated images were always smaller than velocities found by simulation. Relative errors (4% to 25%) depended on the vessel radius and on the shape of the velocity profile, but not on the hematocrit or on the maximum velocity.

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.

How this classification was reachedexpand

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.330

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 head0.039
GPT teacher head0.324
Teacher spread0.286 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2008
Admission routes1
Has abstractyes

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