Fast Interactive Integration of Cross-Sectional Image Datasets and Surface Data for Morphometric Analysis
Bibliographic record
Abstract
To investigate external facial morphology and cell proliferation patterns and their relationship with cleft lip malformation in mice, we need to compare samples of mice tissue photographs and surface reconstructions from micro-CT scans obtained from mouse embryos. Tissue samples obtained through digital photography are typically misaligned with respect to each other, which prevents further analysis. We have developed a system for fast interactive alignment of these image stacks for volume reconstruction and data visualization and analysis in 3D. The system is designed to work in multiprocessor environments and can utilize an arbitrary number of processors, cutting down significantly the turnaround time and allowing users to quickly process sets of hundreds of high resolution images using a combination of automated and interactive tools. Additional modules are used to reconstruct the shape of the original subject. Our system is interactive, fully scalable and can be applied to any photographic sliced dataset, regardless of subject and reduces significantly the processing time for stack alignment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".