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Record W2148150519 · doi:10.1109/iembs.2008.4650429

Global registration of multiple bone fragments using statistical atlas models: Feasibility experiments

2008· article· en· W2148150519 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotics and Sensor-Based Localization
Canadian institutionsQueen's University
Fundersnot available
KeywordsAtlas (anatomy)Computer scienceCadaverPrincipal component analysisArtificial intelligenceData setImage registrationComputer visionPattern recognition (psychology)AnatomyMedicine

Abstract

fetched live from OpenAlex

We propose a new technique to automatically register multiple bone fragments of a fracture using a global registration method guided by a statistical anatomical atlas model. The atlas is created by using the principal component analysis of three-dimensional meshes generated from the Computed Tomography (CT) data of five left femur bone cadavers. The fracture fragments are initially aligned to the generated atlas by using a local point descriptor which can robustly identify a set of potential corresponding points among fragments and the mean atlas model. A global registration algorithm is then utilized to fine-tune the alignment among the fractures and the mean atlas model. Performed experimental simulations on the human femur bone cadavers verify the feasibility of the proposed registration 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.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.485
Threshold uncertainty score0.412

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 head0.082
GPT teacher head0.288
Teacher spread0.206 · 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

Quick stats

Citations30
Published2008
Admission routes1
Has abstractyes

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