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Record W2060247851 · doi:10.1080/1025584031000065956

A Biplanar Reconstruction Method Based on 2D and 3D Contours: Application to the Distal Femur

2003· article· en· W2060247851 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

VenueComputer Methods in Biomechanics & Biomedical Engineering · 2003
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
Keywords3D reconstructionRadiographyProjection (relational algebra)Computer visionFemurArtificial intelligenceIterative reconstructionComputer scienceSurface reconstructionMathematicsSurface (topology)GeometryGeologyAlgorithmMedicineRadiology

Abstract

fetched live from OpenAlex

A three-dimensional (3D) reconstruction algorithm based on contours identification from biplanar radiographs is presented. It requires, as technical prerequisites, a method to calibrate the biplanar radiographic environment and a surface generic object (anatomic atlas model) representing the structure to be reconstructed. The reconstruction steps consist of: the definition of anatomical regions, the identification of 2D contours associated to these regions, the calculation of 3D contours and projection onto the radiographs, the associations between points of the X-rays contours and points of the projected 3D contours, the optimization of the initial solution and the optimized object deformation to minimize the distance between X-rays contours and projected 3D contours. The evaluation was performed on 8 distal femurs comparing the 3D models obtained to CT-scan reconstructions. Mean error for each distal femur was 1 mm.

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)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.806
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.283
Teacher spread0.275 · 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