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Record W3011607949 · doi:10.1117/12.2544469

Quantitative multi-contrast in vivo rodent imaging with optical coherence tomography and angiography (Conference Presentation)

2020· article· en· W3011607949 on OpenAlexaff
Destiny Hsu, Ji Hoon Kwon, Ringo Ng, Jun Song, Myeong Jin Ju, Marinko V. Šarunic

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsRetinal pigment epitheliumRetinaOptical coherence tomographyRetinalContrast (vision)Optical coherence tomography angiographyMelaninIn vivoPreclinical imagingAngiographyOpticsBiomedical engineeringOphthalmologyBiologyMedicineRadiologyPhysics

Abstract

fetched live from OpenAlex

Contrast for imaging the retinal microvasculature and the retinal pigment epithelium (RPE) is essential for pre-clinical studies of vision-robbing diseases. By integrating polarization diversity detection (PDD) with OCT angiography (OCTA), we have developed a novel quantitative multi-contrast OCT for imaging pigment in the RPE as well as flow in the retinal capillaries. An adaptive filter was developed for degree of polarization uniformity (DOPU) processing to provide improved measurements of melanin region thickness in tilted retina. The retinas of three mouse strains were imaged in vivo, with results demonstrating potential for simultaneous mapping of vasculature and melanin distribution in the RPE.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.016
GPT teacher head0.256
Teacher spread0.240 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2020
Admission routes1
Has abstractyes

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