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Record W1887452887 · doi:10.5565/rev/elcvia.165

Principal Deformations Modes of Articulated Models for the Analysis of 3D Spine Deformities

2008· article· en· W1887452887 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueELCVIA Electronic Letters on Computer Vision and Image Analysis · 2008
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustinePolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchNature
KeywordsPrincipal component analysisArtificial intelligenceContext (archaeology)VertebraComputer scienceMathematicsCovarianceGeneralizationRotation (mathematics)Computer visionPattern recognition (psychology)Mathematical analysisAnatomyGeologyMedicine

Abstract

fetched live from OpenAlex

Articulated models are commonly used for recognition tasks in robotics and in gait analysis, but canalso be extremely useful to develop analytical methods targeting spinal deformities studies. The threedimensionalanalysis of these deformities is critical since they are complex and not restricted to a givenplane. Thus, they cannot be assessed as a two-dimensional phenomenon. However, analyzing large databasesof 3D spine models is a difficult and time-consuming task. In this context, a method that automatically extractsthe most important deformation modes from sets of articulated spine models is proposed.The spine was modeled with two levels of details. In the first level, the global shape of the spine wasexpressed using a set of rigid transformations that superpose local coordinates systems of neighboring vertebrae.In the second level, anatomical landmarks measured with respect to a vertebra’s local coordinatesystem were used to quantify vertebra shape. These articulated spine models do not naturally belong to avector space because of the vertebral rotations. The Fréchet mean, which is a generalization of the conventionalmean to Riemannian manifolds, was thus used to compute the mean spine shape. Moreover, ageneralized covariance computed in the tangent space of the Fréchet mean was used to construct a statisticalshape model of the scoliotic spine. The principal deformation modes were then extracted by performing aprincipal component analysis (PCA) on the generalized covariance matrix.The principal deformations modes were computed for a large database of untreated scoliotic patients.The obtained results indicate that combining rotation, translation and local vertebra shape into a unifiedframework leads to an effective and meaningful analysis method for articulated anatomical structures. Thecomputed deformation modes also revealed clinically relevant information. For instance, the first mode ofdeformation is associated with patients’ growth, the second is a double thoraco-lumbar curve and the thirdis a thoracic curve. Other experiments were performed on patients classified by orthopedists with respect toa widely used two-dimensional surgical planning system (the Lenke classification) and patterns relevant tothe definition of a new three-dimensional classification were identified. Finally, relationships between localvertebrae shapes and global spine shape (such as vertebra wedging) were demonstrated using a sample of3D spine reconstructions with 14 anatomical landmarks per vertebra.KeyWords: Shape Analysis, Articulated Models, Spinal Deformities, Scoliosis, 3D Reconstruction, Surgical Classifications.

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: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
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 head0.015
GPT teacher head0.275
Teacher spread0.259 · 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