Atlas Construction and Validation Using Cone-Beam Computed Tomography Images of Healthy Adult Human Hands
Bibliographic record
Abstract
Anatomical atlases provide standardized spatial references for comparing complex structures. This study develops a high-resolution anatomical atlas of healthy hand bones using cone-beam computed tomography (CBCT), which provides enhanced spatial resolution and lower radiation dose compared to conventional CT. The atlas, constructed from CBCT bilateral scans of 18 healthy adult subjects (35 total hands), serves as a standardized spatial reference for bones in the carpal, metacarpal, and proximal phalangeal regions, where variations in individual scans can complicate analysis. An iterative registration and averaging process, coupled with image preprocessing, enabled the construction of the atlas. The atlas was validated using shape analysis of each bone in the atlas compared to the input images. Validation showed a mean z-score of 0.003 across metrics (volume, surface area, axis lengths, elongation, and flatness), confirming anatomical accuracy. Atlas-based segmentation accuracy, assessed on three independent images, yielded a Dice coefficient of 0.94 and Hausdorff distance of 1.73 mm. Additionally, a subtraction-based method applied to rheumatoid arthritis (RA)-affected joints achieved a mean erosion volume difference of 4.9% compared to manual annotations. This CBCT-based atlas is a valuable reference for segmentation and analysis in bone-related disease research, particularly for tracking RA-induced changes in bone morphology.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".