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Record W2963697984 · doi:10.1117/12.2526652

Dual-beam manually-actuated catheters for wide-field distortion-corrected optical imaging

2019· article· en· W2963697984 on OpenAlexaff
Anthony M. D. Lee, Andrea Manjarres, Calum MacAulay, Pierre Lane

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsOccupational Cancer Research Centre
Fundersnot available
KeywordsDistortion (music)Optical imagingBeam (structure)OpticsDual (grammatical number)Computer scienceField (mathematics)Depth of fieldMaterials sciencePhysicsOptoelectronicsMathematics

Abstract

fetched live from OpenAlex

Previously, we have demonstrated two fiber optic catheter implementations of dual-beam manually-actuated distortion-corrected imaging (DMDI) that employ rotationally-scanning micromotors that are potentially suitable for endoscopic imaging applications. These catheters are able to correct for both axial (push/pull) and azimuthal (catheter rotation) manual actuation. We presented a multiplexed dual beam micromotor catheter (mDBMC), an OCT-specific design that depth-multiplexes both imaging channels within a single optical fiber and a dual-fiber parallel DBMC (pDBMC) design that is suitable for any point-scanning modality such as OCT, fluorescence, or reflectance imaging. As the mDBMC has a relatively simple scan pattern, we developed a straightforward automated method for distortion correction based on image frame cross-correlation. Due to the complicated scan pattern of the pDBMC, we initially co-registered image features for the pDMBC in a time-consuming manual process. In this work, we describe our efforts to develop an automated distortion correction method for the pDMBC. We demonstrate initial success in automated correction along the pullback dimension.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.680

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 head0.004
GPT teacher head0.209
Teacher spread0.205 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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