Advancing X-ray microcomputed tomography image processing of avian eggshells: An improved registration metric for multiscale 3D images and resolution-enhanced segmentation of eggshell pores using edge-attentive neural networks
Bibliographic record
Abstract
Avian eggs exhibit a variety of shapes and sizes, reflecting different reproductive strategies. The eggshell not only protects the egg contents, but also regulates gas and water vapor exchange vital for embryonic development. While many studies have explored eggshell ultrastructure, the distribution of pores across the entire shell is less well understood because of a trade-off between resolution and field-of-view in imaging. To overcome this, a neural network was developed for resolution enhancement of low-resolution 3D tomographic data, while performing voxel-wise labeling. Trained on X-ray microcomputed tomography images of ostrich, guillemot and crow eggshells from a natural history museum collection, the model used stepwise magnification to create low- and high-resolution training sets. Registration performance was validated with a novel metric based on local grayscale gradients. An edge-attentive loss function prevented bias towards the dominant background class (95% of all voxels), ensuring accurate labeling of eggshell (5%) and pore (0.1%) voxels. The results indicate that besides edge-attention and class balancing, 3D context preservation and 3D convolution are of paramount importance for extrapolating subvoxel features. • A neural network enables 3D image upsampling combined with voxel-wise segmentation. • Eggshells from a natural history collection were imaged using multiscale X-ray μ CT. • We report a novel edge-based registration algorithm for multiscale 3D images. • Subvoxel eggshell pore distribution and architecture are visualized in 3D images.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".