Morphometric analysis of trabecular bone thickness using different algorithms
Bibliographic record
Abstract
Investigations have been carried out with the goal of assessing the trabecular bone thickness of biological samples using images obtained by micro-computed tomography and magnetic resonance imaging. There is no conventional definition of trabecular bone thickness, and many methods may be involved in determining it. However, the results of the available algorithms or software packages differ considerably from each other. This paper determines trabecular bone thickness on the basis of several algorithms. A deep understanding of the performance of different methods is achieved by studying pseudo-three-dimensional images of both geometrical models of well-defined thickness and real bone samples with different bone densities. The models facilitate comparisons between the algorithms or software packages. Comparison of the results obtained from these commercial software packages and other state-of-the-art algorithms shows that the thickness, spatial distribution, and shape of an object affect each result differently, but in a significant manner. This is primarily due to variations in the thresholding algorithms used to distinguish object area elements (pixels/voxels) from the background, or non-object, region. Additionally, the results show that the average difference in thickness measurements can vary by up to 102.34% for models and 46.49% for real bone samples. This data shows that the differences in measurements of the trabecular bone thickness due simply to the algorithm involved are remarkable. Therefore, biomedical engineers and scientists should be careful to select the algorithm that is most compatible with their specific application.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| 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) | 0.002 | 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".