A novel 3D mouse embryo atlas based on micro-CT
Bibliographic record
Abstract
The goal of the International Mouse Phenotyping Consortium (IMPC) is to phenotype targeted knockout mouse strains throughout the whole mouse genome (23,000 genes) by 2021. A significant percentage of the generated mice will be embryonic lethal; therefore, phenotyping methods tuned to the mouse embryo are needed. Methods that are robust, quantitative, automated and high-throughput are attractive owing to the numbers of mice involved. Three-dimensional (3D) imaging is a useful method for characterizing morphological phenotypes. However, tools to automatically quantify morphological information of mouse embryos from 3D imaging have not been fully developed. We present a representative mouse embryo average 3D atlas comprising micro-CT images of 35 individual C57BL/6J mouse embryos at 15.5 days post-coitum. The 35 micro-CT images were registered into a consensus average image with our automated image registration software and 48 anatomical structures were segmented manually. We report the mean and variation in volumes for each of the 48 segmented structures. Mouse organ volumes vary by 2.6-4.2% on a linear scale when normalized to whole body volume. A power analysis of the volume data reports that a 9-14% volume difference can be detected between two classes of mice with sample sizes of eight. This resource will be crucial in establishing baseline anatomical phenotypic measurements for the assessment of mutant mouse phenotypes, as any future mutant embryo image can be registered to the atlas and subsequent organ volumes calculated automatically.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.006 | 0.002 |
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".