Fast reconstruction of volumetric models of anatomical structures
Bibliographic record
Abstract
This paper reports a fast and robust method for building 3D volumetric models of anatomical structures. The proposed reconstruction solution is image modality independent. As input, the method requires a set of contours obtained after organ segmentation. It is not necessary for contours to lie on parallel slices. The contours are simplified using a combination of smoothing techniques and B-splines. A 3D triangulation of the object convex hull is generated, based on a 3D Delaunay triangulation algorithm. A sculpting process of the non-object tetrahedra is performed, based on the spatial information obtained in the segmentation process. The surface, represented as a set of triangles, and the volume, represented as a set of tetrahedral, are obtained. To validate the algorithm, geometrical measurements of the generated model are computed, and compared with the original object. Currently, the method is tested on CT and MR images. At this stage, 2 seconds are needed on a standard workstation to build a skull model with approximately 120,000 tetrahedrals.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".