Principal Deformations Modes of Articulated Models for the Analysis of 3D Spine Deformities
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".