Automated pipeline for anatomical phenotyping of mouse embryos using micro-CT
Bibliographic record
Abstract
The International Mouse Phenotyping Consortium (IMPC) plans to phenotype 20,000 single-gene knockout mice to gain an insight into gene function. Approximately 30% of these knockout mouse lines will be embryonic or perinatal lethal. The IMPC has selected three-dimensional (3D) imaging to phenotype these mouse lines at relevant stages of embryonic development in an attempt to discover the cause of lethality using detailed anatomical information. Rate of throughput is paramount as IMPC production centers have been given the ambitious task of completing this phenotyping project by 2021. Sifting through the wealth of data within high-resolution 3D mouse embryo data sets by trained human experts is infeasible at this scale. Here, we present a phenotyping pipeline that identifies statistically significant anatomical differences in the knockout, in comparison with the wild type, through a computer-automated image registration algorithm. This phenotyping pipeline consists of three analyses (intensity, deformation, and atlas based) that can detect missing anatomical structures and differences in volume of whole organs as well as on the voxel level. This phenotyping pipeline was applied to micro-CT images of two perinatal lethal mouse lines: a hypomorphic mutation of the Tcf21 gene (Tcf21-hypo) and a knockout of the Satb2 gene. With the proposed pipeline we were able to identify the majority of morphological phenotypes previously published for both the Tcf21-hypo and Satb2 mutant mouse embryos in addition to novel phenotypes. This phenotyping pipeline is an unbiased, automated method that highlights only those structural abnormalities that survive statistical scrutiny and illustrates them in a straightforward fashion.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".