3D shape asymmetry analysis using correspondence between partial geodesic curves
Bibliographic record
Abstract
Analyzing the asymmetry of anatomical shapes is one of the cornerstones of efficient computerized diagnosis. In the application of scoliotic trunk analysis, one major challenge is the high variability and complexity of deformations due to the pathology itself, and to changes of body poses, for instance, torsos acquired in lateral bending poses for surgical planning. In this paper, we present a novel and fully automatic approach to analyzing the asymmetry of deformable trunk shapes. Our method detects the symmetry plane/axis of the shape and a set of reflective geodesic curves; the method then uses parts of these geodesics to compute point correspondence between any reflective pair. We present a preliminary study on scoliotic torsos in neutral standing and lateral bending poses. Our results show that matching parts of reflective curves is robust against non-isometric deformations. The proposed method can extend to other applications, such as maxillofacial surgery planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".