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Record W2103856694 · doi:10.1109/iembs.2007.4352426

2D/3D Registration of Multiple Bones

2007· article· en· W2103856694 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

VenueConference proceedings · 2007
Typearticle
Languageen
FieldEngineering
TopicMedical Imaging and Analysis
Canadian institutionsQueen's UniversityKingston General Hospital
Fundersnot available
KeywordsComputer visionArtificial intelligenceImage registrationRadiographyWristComputer scienceObject (grammar)Carpal bonesMatching (statistics)Image (mathematics)MedicineAnatomyRadiology

Abstract

fetched live from OpenAlex

The problem of 2D/3D registration is, given a 3D image of an object and one or more 2D images of the object in known poses, to recover the 3D pose of the object. We propose a solution for registering multiple bones in 2D radiographic images and 3D CT images using normalized correlation coefficient template matching. We performed tests using synthetic radiographs and CT volumes of two knees and one wrist. We obtained good registration results (less than 2 degrees and 2 mm registration error) for all of the larger bones, but were unable to successfully register the small carpal bones of the wrist with high accuracy.

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: Empirical
Teacher disagreement score0.566
Threshold uncertainty score0.279

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 head0.021
GPT teacher head0.240
Teacher spread0.219 · 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