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Record W2101636005 · doi:10.1109/icassp.2002.5745501

Wavelet-based automatic segmentation of the vertebral bodies in digital radiographs

2002· article· en· W2101636005 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

VenueIEEE International Conference on Acoustics Speech and Signal Processing · 2002
Typearticle
Languageen
FieldEngineering
TopicMedical Imaging and Analysis
Canadian institutionsÉcole de Technologie SupérieureCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsArtificial intelligenceSegmentationWaveletComputer scienceComputer visionRadiographyPattern recognition (psychology)CurvatureImage segmentationsortMathematicsRadiologyMedicine

Abstract

fetched live from OpenAlex

This paper proposes a multi-resolution analysis (MRA) approach to vertebral body segmentation in digital radiographs. A wavelet-based technique carries out the extraction of the most significant features in the radiographs. It generates a multi-resolution edge characterization, discarding noisy artifacts in the meantime. Specific features classification and geometric alignment is then performed, using statistical data to build the. models and curvature analysis to sort the edges. An active contour scheme generates the final contours to be used in clinical studies of the spine.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.973
Threshold uncertainty score0.274

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 head0.025
GPT teacher head0.251
Teacher spread0.226 · 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