Predicting rat lumbar vertebral failure patterns as synthetic μCT images using a deep convolutional generative adversarial network
Bibliographic record
Abstract
This study aims to develop a generative deep learning (DL) model to predict fracture patterns through creation of synthetic 3D μCT images of fractured rat lumbar vertebrae. The proposed model is a 3D conditional generative adversarial network (cGAN). The dataset contained sequential 3D μCT images of rat lumbar vertebrae acquired during axial compressive loading (unloaded, after 1500 μm of displacement, after fracture). Three experiments were run: unloaded input predicting 1500 μm, 1500 μm predicting fracture, and unloaded predicting fracture. The cGAN was trained on 64 μCT images of rat lumbar vertebral motion segments and was validated on 8 images. Quantitative metrics (dice similarity coefficient (DSC), Jaccard index (JAC), Fréchet inception distance (FID), structural similarity index measure (SSIM)) assessed predicted image quality. Qualitative measures investigating fracture location and disease severity (or lack thereof) were also assessed. The unloaded to 1500 μm experiment generated realistic examples of loaded (unfractured) rat vertebrae. These included maintenance of the presence of metastatic disease when relevant and yielded the best quantitative metrics. The 1500 μm to fracture experiment performed significantly better on the FID and SSIM metrics than the unloaded to fracture configuration. The 1500 μm to fracture experiment predicted more true positive fractures and fewer false negative fractures than the unloaded to fracture experiment. Both fracture experiments had a low false positive fracture prediction rate (<10 %). The presented cGAN generates realistic rat lumbar vertebrae failure patterns as 3D μCT images and shows promise for future generative DL applications to model damage behaviour of biological structures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".