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Personalized 3D reconstruction of the rib cage for clinical assessment of trunk deformities

2013· article· en· W2131736271 on OpenAlexafffund
Sébastien Grenier, Stefan Parent, Farida Chériet

Bibliographic record

VenueMedical Engineering & Physics · 2013
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsPolytechnique MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersNatural Sciences and Engineering Research Council of CanadaFonds de recherche du Québec – Nature et technologiesCanadian Institutes of Health Research
KeywordsRib cageSegmentation3D reconstructionComputer scienceDeformityTrunkRadiographyScoliosisArtificial intelligenceTorsoComputer visionAnatomyMedicineRadiologySurgery

Abstract

fetched live from OpenAlex

Scoliosis is a 3D deformity of the spine and rib cage. Extensive validation of 3D reconstruction methods of the spine from biplanar radiography has already been published. In this article, we propose a novel method to reconstruct the rib cage, using the same biplanar views as for the 3D reconstruction of the spine, to allow clinical assessment of whole trunk deformities. This technique uses a semi-automatic segmentation of the ribs in the postero-anterior X-ray view and an interactive segmentation of partial rib edges in the lateral view. The rib midlines are automatically extracted in 2D and reconstructed in 3D using the epipolar geometry. For the ribs not visible in the lateral view, the method predicts their 3D shape. The accuracy of the proposed method has been assessed using data obtained from a synthetic bone model as a gold standard and has also been evaluated using data of real patients with scoliotic deformities. Results show that the reconstructed ribs enable a reliable evaluation of the rib axial rotation, which will allow a 3D clinical assessment of the spine and rib cage deformities.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.256

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 head0.035
GPT teacher head0.344
Teacher spread0.309 · 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 designObservational
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

Citations11
Published2013
Admission routes2
Has abstractyes

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