Automated 3-D Reconstruction of the Surface of Live Early-Stage Amphibian Embryos
Bibliographic record
Abstract
Although three-dimensional (3-D) reconstructions of the surfaces of live embyos are vital to understanding embryo development, morphogenetic tissue movements and other factors have prevented the automation of this task. Here, we report an integrated set of software algorithms that overcome these challenges, making it possible to completely automate the reconstruction of embryo surfaces and other textured surfaces from multiview images. The process involves: 1) building accurate point correspondences using a robust deformable template block matching algorithm; 2) removing outliers using fundamental matrix calculations in conjunction with a RANSAC algorithm; 3) generating 3-D point clouds using a bundle adjustment algorithm that includes camera position and distortion corrections; 4) meshing the point clouds into triangulated surfaces using a Tight Cocone algorithm that produces water tight models; 5) refining surfaces using midpoint insertion and Laplacian smoothing algorithms; and 6) repeating these steps until a measure of convergence G, the rms difference between successive reconstructions, is below a specified threshold. Reconstructions were made of 2.2-mm diameter, neurulation-stage axolotl (amphibian) embryos using 44 multiview images collected with a robotic microscope. A typical final model (sixth iteration) contained 3787 points and 7562 triangles and had an error measure of G = 5.9 microm.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".