Automatic 3D Segmentation of Hydrogel Scaffolds Based on PBI-µCT
Bibliographic record
Abstract
Hydrogel scaffolds are a promising biomaterial used in tissue engineering and regenerative medicine. Scaffolds can be constructed using 3D bioprinting techniques which allow for intricate architectures at the site of injury. However, 3D segmentation of hydrogel scaffolds remains a challenge due to the low-density of hydrogels exhibiting poor image contrast. One promising method is to use synchrotron radiation (SR) propagation-based imaging (PBI) microcomputed tomography (μCT). This method shows the phase shift of X-rays between sample and detector with strong edge enhancement. Quantitatively, this phase shift can be calculated using phase retrieval (PR) algorithm which converts the edge enhancement into area where the X-ray propagated through the sample but will reduce sharpness between across boundaries. Alternatively, image denoising, e.g., Noise2Inverse (N2I), can be used to maximize the edge enhancement but does not represent the area. Thus, SR-PBI-μCT data can be processed in complementary ways. A convolutional neural network (CNN) can be trained to learn the complementary area and edge information which allow for more accurately represented result than either method alone. This provides efficient 3D segmentation without manual input or pre-existing reference.
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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) | 0.001 | 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) | 0.002 | 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".