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Record W4293098605 · doi:10.1117/12.2609913

Progress on multimodal retinal Imaging via OCT multi-volume averaging and two photon excited fluorescence

2022· article· en· W4293098605 on OpenAlexaff
William Newberry, Laura Vargas, Marinko V. Šarunic

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsWavefrontSIGNAL (programming language)Modality (human–computer interaction)Computer scienceOpticsRetinaAdaptive opticsExcited stateFluorescencePhysicsComputer visionArtificial intelligence

Abstract

fetched live from OpenAlex

We present our progress on multi-modal imaging in the mouse retina using OCT and Two-Photon Excited Fluorescence (TPEF). In order to progress this modality towards a clinical setting, the power incident on the retina must be reduced. With a significantly dimmer TPEF signal, motion corrected registration and averaging becomes difficult. We have developed an approach to utilize multi-modal simultaneous acquisition to non-rigidly register both datasets solely using the OCT signal. Image quality is further enhanced by correcting wavefront aberrations introduced from the high NA configuration through a sensorless image-based hill-climbing algorithm.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.898
Threshold uncertainty score0.851

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 head0.008
GPT teacher head0.236
Teacher spread0.228 · 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 designSimulation or modeling
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
Published2022
Admission routes1
Has abstractyes

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