Thickness analysis and reconstruction of trabecular bone and bone substitute microstructure based on fuzzy distance map using both ridge and thinning skeletonization
Bibliographic record
Abstract
The accurate geometric analysis of microstructured biological porous media is crucial for an understanding of the geometric changes that result from diseases such as osteoporosis and for the design of bone substitutes for the treatment of cancer patients. This paper presents a methodological development designed to improve the description of the average pore size and thickness of a micro structure's biological media. Specifically, the paper introduces a new skeletonization method based on a ridge skeleton combined with fuzzy distance transform (FDT), which has recently been used in the literature and has shown some advantages compared to the traditional distance transform. The new skeletonization method is applied to trabecular bone excised from healthy and osteoporotic vertebrae, as well as to bone substitutes with small and large pores. These samples are scanned by a micro-computed tomography scanner. The new skeletonization method has been implemented successfully, and an exact algorithm for implementation and reconstruction has been developed. The results show that, compared to widely used thinning methods, the new FDT ridge skeleton generates measurements that are more representative of the microstructure of the examined media. It is concluded that the new method can find the ridges of the FDT and produce topologically accurate skeletons, leading to accurate measurement and reconstruction of the microstructured porous media.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".