Characterization and quantification of in-vitro equine bone resorption in 3D using μCT and deep learning-aided feature segmentation
Bibliographic record
Abstract
High cyclic strains induce formation of microcracks in bone, triggering targeted bone remodeling, which entails osteoclastic resorption. Racehorse bone is an ideal model for studying the effects of high-intensity loading, as it is subject to focal formation of microcracks and subsequent bone resorption. The volume of resorption in vitro is considered a direct indicator of osteoclast activity but indirect 2D measurements are used more often. Our objective was to develop an accurate, high-throughput method to quantify equine osteoclast resorption volume in μCT 3D images. Here, equine osteoclasts were cultured on equine bone slices and imaged with μCT pre- and postculture. Individual resorption events were then isolated and analyzed in 3D. Modal volume, maximum depth, and aspect ratio of resorption events were calculated. A convolutional neural network (CNN U-Net-like) was subsequently trained to identify resorption events on post-culture μCT images alone, without the need for pre-culture imaging, using archival bone slices with known resorption areas and paired CTX-I biomarker levels in culture media. 3D resorption volume measurements strongly correlated with both the CTX-I levels (p < 0.001) and area measurements (p < 0.001). Our 3D analysis shows that the shapes of resorption events form a continuous spectrum, rather than previously reported pit and trench categories. With more extensive resorption, shapes of increasing complexity appear, although simpler resorption cavity morphologies (small, rounded) remain most common, in acord with the left-hand limit paradigm. Finally, we show that 2D measurements of in vitro osteoclastic resorption are a robust and reliable proxy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".